Cloud-based vehicular telematics systems and methods for generating hybrid epoch driver predictions using edge-computing

ABSTRACT

Method and system for generating a hybrid epoch score for a user using edge-computing. In some examples, a computer-implemented method includes: retrieving, from at least one of an edge-computing device and a server, prior telematics data indicative of the operation of a vehicle by the user during one or more prior trips in a prior epoch; collecting, using one or more sensors of the edge-computing device, recent telematics data indicative of the operation of the vehicle by the user during one or more recent trips in a current epoch; generating, using the edge-computing device, a prior epoch score based at least in part upon the prior telematics data; generating, using the edge-computing device, a partial current epoch score based at least in part upon the recent telematics data; and generating, using the edge-computing device, a hybrid current epoch score based at least in part upon the prior epoch score and the partial current epoch score.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims priority to U.S. Patent Application No.62/909,508 filed Oct. 2, 2019, which is incorporated by reference hereinfor all purposes.

The following four applications, including this one, are being filedconcurrently and the other three are hereby incorporated by reference intheir entirety for all purposes:

1. U.S. patent application Ser. No. ______, titled “Cloud-BasedVehicular Telematics Systems and Methods for Generating Hybrid EpochDriver Predictions” (Attorney Docket Number BOL-32817-54388A-NP1);

2. U.S. patent application Ser. No. ______, titled “Cloud-BasedVehicular Telematics Systems and Methods for Generating Hybrid EpochDriver Predictions Using Edge-Computing” (Attorney Docket NumberBOL-32817-54388B-NP1);

3. U.S. patent application Ser. No. ______, titled “Cloud-BasedVehicular Telematics Systems and Methods for Generating Hybrid EpochDriver Predictions and Driver Feedback” (Attorney Docket NumberBOL-32817-54388C-NP1); and

4. U.S. patent application Ser. No. ______, titled “Cloud-BasedVehicular Telematics Systems and Methods for Generating Hybrid EpochDriver Predictions and Hazard Alerts” (Attorney Docket NumberBOL-32817-54388D-NP1).

FIELD OF THE DISCLOSURE

Some embodiments of the present disclosure are directed to cloud-basedvehicular telematics systems and methods. More particularly, certainembodiments of the present disclosure provide methods and systems forcloud-based vehicular telematics systems and methods for generatinghybrid epoch driver predictions using edge-computing. Merely by way ofexample, the present disclosure has been applied to telematics data, butit would be recognized that the present disclosure has much broaderrange of applicability.

BACKGROUND OF THE DISCLOSURE

Telematics information regarding operation of passenger vehicles maygenerally be collected for vehicle trips. The collected telematics datamay be used in variable vehicular insurance pricing schemes, includingpay-per-mile or pay-per-use, where a vehicle's trips, miles, orotherwise distance traveled, at some future time period may be computedbased on the telematics data. An invoice, such as an insurance invoice,may then be prepared for a given vehicle or user based on the vehicletrip(s). A problem arises, however, with respect to the variabilityand/or uncertainty of such pay-per-mile or pay-per-use schemes becauseend-users are unable to determine the value of future invoices or otherdriver metrics based on current data. This can be especially true whentelematics data are collected at high volume or high fidelity rates thatcan include the generation and collection of large numbers of records,such as tens of thousands of telematics records. With modern datacollection techniques, such high volume and/or high fidelity telematicsrecords can be generated even for short vehicle trips. For at least theforegoing reasons, there is a need for cloud-based vehicular telematicssystems and methods for generating hybrid epoch driver predictions.

BRIEF SUMMARY OF THE DISCLOSURE

Some embodiments of the present disclosure are directed to cloud-basedvehicular telematics systems and methods. More particularly, certainembodiments of the present disclosure provide methods and systems forcloud-based vehicular telematics systems and methods for generatinghybrid epoch driver predictions using edge-computing. Merely by way ofexample, the present disclosure has been applied to telematics data, butit would be recognized that the present disclosure has much broaderrange of applicability.

According to various embodiments, a computer-implemented method forgenerating a hybrid epoch score for a user using edge-computingincludes: retrieving, from an edge-computing device and/or a server,prior telematics data indicative of the operation of a vehicle by theuser during one or more prior trips in a prior epoch; collecting, usingone or more sensors of the edge-computing device, recent telematics dataindicative of the operation of the vehicle by the user during one ormore recent trips in a current epoch; generating, using theedge-computing device, a prior epoch score based at least in part uponthe prior telematics data; generating, using the edge-computing device,a partial current epoch score based at least in part upon the recenttelematics data; and generating, using the edge-computing device, ahybrid current epoch score based at least in part upon the prior epochscore and the partial current epoch score.

According to various embodiments, a system for generating a hybrid epochscore for a user using edge-computing includes: a data retrieving moduleconfigured to: retrieve, from an edge-computing device and/or a server,prior telematics data indicative of the operation of a vehicle by theuser during one or more prior trips in a prior epoch; a data collectingmodule configured to: collect, using one or more sensors of theedge-computing device, recent telematics data indicative of theoperation of the vehicle by the user during one or more recent trips ina current epoch; and an epoch score generating module configured to:generate, using the edge-computing device, a prior epoch score based atleast in part upon the prior telematics data; generate, using theedge-computing device, a partial current epoch score based at least inpart upon the recent telematics data; and generate, using theedge-computing device, a hybrid current epoch score based at least inpart upon the prior epoch score and the partial current epoch score.

According to various embodiments, a non-transitory computer-readablemedium with instructions stored thereon, that upon execution by aprocessor, causes the processor to perform: retrieving, from anedge-computing device and/or a server, prior telematics data indicativeof the operation of a vehicle by the user during one or more prior tripsin a prior epoch; collecting, using one or more sensors of theedge-computing device, recent telematics data indicative of theoperation of the vehicle by the user during one or more recent trips ina current epoch; generating, using the edge-computing device, a priorepoch score based at least in part upon the prior telematics data;generating, using the edge-computing device, a partial current epochscore based at least in part upon the recent telematics data; andgenerating, using the edge-computing device, a hybrid current epochscore based at least in part upon the prior epoch score and the partialcurrent epoch score.

Depending upon the embodiment, one or more benefits may be achieved.These benefits and various additional objects, features and advantagesof the present disclosure can be fully appreciated with reference to thedetailed description and accompanying drawings that follow.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a simplified diagram showing a telematics cloud platformconfigured to receive vehicular telematics data from a telematics deviceonboard a vehicle, according to various embodiments of the presentdisclosure.

FIG. 1B is a simplified diagram showing a block diagram of a telematicscloud platform and a telematics device, according to various embodimentsof the present disclosure.

FIG. 2 is a simplified diagram showing a data transmission andimplementation diagram of a cloud-based vehicular telematics system forgenerating hybrid epoch driver predictions, according to variousembodiments of the present disclosure.

FIG. 3 is a simplified diagram showing a telematics mobile app renderinga guided user interface (GUI) on a display of a telematics device,according to various embodiments of the present disclosure.

FIG. 4 is a simplified diagram showing a telematics method forgenerating hybrid epoch driver predictions using edge-computing,according to various embodiments of the present disclosure.

FIG. 5 is a simplified diagram showing a system for generating a hybridepoch score using edge-computing, according to various embodiments ofthe present disclosure.

FIG. 6 is a simplified diagram showing a method for generating a hybridepoch score using edge-computing, according to various embodiments ofthe present disclosure.

FIG. 7 is a simplified diagram showing a computer device, according tovarious embodiments of the present disclosure.

FIG. 8 is a simplified diagram showing a computer system, according tovarious embodiments of the present disclosure.

DETAILED DESCRIPTION OF THE DISCLOSURE

Some embodiments of the present disclosure are directed to cloud-basedvehicular telematics systems and methods. More particularly, certainembodiments of the present disclosure provide methods and systems forcloud-based vehicular telematics systems and methods for generatinghybrid epoch driver predictions using edge-computing. Merely by way ofexample, the present disclosure has been applied to telematics data, butit would be recognized that the present disclosure has much broaderrange of applicability.

In various embodiments, the present disclosure provides solutions to thehigh volume and/or high fidelity data rates typically involved with thegeneration and/or collection of vehicular telemetry data. In variousexamples, the present disclosure provides a variable insurance pricingsystem where end-users, such as vehicle drivers, may, such as on acontinuous basis, and upon the generation of continued vehiculartelematics data, receive an expected or predicted, future rate, and/orother prediction analytics, for a next cycle (e.g., a next billingcycle, such as a next month) in real-time, near real-time, or followingthe completion of one or more vehicle trips. Accordingly, in variousembodiments disclosed herein, cloud-based vehicular telematics systemsand methods are disclosed for generating hybrid epoch driverpredictions.

In various embodiments, the vehicular telematics systems and methods mayinclude a telematics mobile application configured to execute on atelematics device. The telematics mobile application may be configuredto collect telematics data from one or more sensors during one or morevehicle trips. In some embodiments, the sensors may be part of thetelematics device itself. In certain embodiments, the sensors may bepart of a vehicle associated with the one or more vehicle trips, wherethe sensors transmit telematics data wirelessly (or wired) to thetelematics device. In some examples, the telematics mobile applicationmay render or generate a guided user interface (GUI) on a display of thetelematics device. The GUI may include one or more screens, including aGUI control screen and/or controls, to allow for manipulation of thetelematics mobile application by a user, such as by a driver of one ormore vehicles associated with one or more vehicle trips.

In various embodiments, the vehicular telematics systems and methods mayinclude a telematics server communicatively coupled, via a computernetwork, to the telematics mobile application. In some examples, thetelematics server is configured to receive the telematics data from themobile app and/or telematics device during the one or more vehicletrips. In various examples, the telematics server is configured toexecute instructions causing the telematics server to implement one ormore algorithms for generating epoch-based vehicle or driverpredictions, including artificial intelligence vehicle or driverpredictions as described herein. In various examples, the telematicsserver is configured to receive the telematics data from the telematicsdevice. In some embodiments, the telematics data may be pushed to thetelematics server from the telematics mobile application. In certainembodiments, the telematics data may be pulled by the telematics server,upon request by the telematics server, from the telematics mobileapplication.

In various embodiments, the telematics server is configured to generatea hybrid epoch driver score based at least in part upon a previous epochdriver score and a predicted driver epoch score. In some examples, theprevious epoch score may be generated, by the telematics server, for aprevious epoch (e.g., a previous month, week, etc.) from telematics datadefined over one or more actual vehicle trips of the driver during theprevious epoch. In some embodiments, the previous epoch score may relateto, or be used to generate, a rate or discount associated with a productor service (e.g., a rate or discount associated with an insurance policyof the driver). In various examples, the generating or a rate ordiscount is based on hybrid epoch score and/or information outside ofthe epoch, such as historical scores and/or data.

In various embodiments, the predicted epoch driver score may begenerated, by the telematics server, for a current epoch (e.g., acurrent month, week, etc.). The predicted epoch driver score may bedetermined for a remaining (future) time period of the current epoch. Insome examples, the predicted epoch driver score may be determined from(1) telematics data defined over one or more actual vehicle trips of thedriver during the current epoch, and/or (2) telematics data predictedover one or more expected vehicle trips of the driver during the currentepoch. In some examples, one or more expected vehicle trips (or totaltime value for such trips) is predicted for the driver for the remaining(future) current epoch time (e.g., the period of time for the currentepoch that has not yet elapsed). The expected set of vehicle trips maybe determined based at least in part upon the driver's past drivingbehavior as determined from past telematics data (e.g., from telematicsdata of one or more previous epochs). In various embodiments, quantitymetric and/or quality metric of the user's past driving behavior may begenerated using artificial intelligence or similar techniques, includingstatistical or regression analysis, machine-learning, natural networks,etc.

In various embodiments, the telematics server may generate the hybridepoch driver score from portions of each of the previous epoch driverscore and a predicted driver epoch score. In various examples, ablended, weighted, or otherwise hybrid score is generated by one or morealgorithms using each of the previous epoch driver score and a predicteddriver epoch score. In some examples, a proportionate predicted (future)epoch driver score is determined for the current epoch and is weightedor proportioned according to the remaining current epoch time.Similarly, a proportionate previous epoch driver score is determined forthe previous epoch and is also weighted or proportioned according to theremaining current epoch time. A greater remaining current epoch time maycause a greater weighting or proportion of the previous epoch driverscore to influence the hybrid epoch driver score. In contrast, a lesserremaining current epoch time may cause a lesser weighting or proportionof the previous epoch driver score to influence the hybrid epoch driverscore.

In various examples, the telematics server may transmit to thetelematics mobile application the hybrid epoch driver score. Thetransmission may be made in real-time or near real-time as the driveroperates during, and/or completes, vehicle trips. The telematics mobileapplication may be configured to display the hybrid epoch driver scoreon a display via a GUI.

In various embodiments, the telematics server is configured to generatea previous epoch score based at least in part upon telematics datareceived by the telematics server from the telematics mobile applicationof a driver during a previous epoch (e.g., a previous month or period).The previous epoch score may be used as a starting factor, predictionvalue, or feature value, for a current epoch's (e.g., a current month's)predicted epoch driver score. In some examples, the starting predictionmay be a feature input data value used to train or generate a machinelearning model. Such prediction value may be updated after each trip anend-user (e.g., a vehicle driver) completes (or after each evening, ifthe drives infrequently). The prediction may incorporate computations,by the telematics server, that predict, the remainder of the epoch timeperiod (e.g., month) based at least in part upon an assumption that anend-user would operate a vehicle at the same quantity/quality level asthe user's past driving month. In some examples, the telematics serveris configured to predict a driver's hybrid epoch driver score as thedriver continues to drive, during the current epoch, based at least inpart upon the quantity and/or quality the driver has driven in aprevious epoch. In certain examples, the predicted epoch driver score(e.g., the score that factors the amount of driving the driver willperform for the rest of the month) is determined from the amount ofproportional driving the driver performed in the prior epoch (e.g., inthe prior month). In certain examples, the predicted epoch driver scoremay be determined from the actual vehicle trips, and related telematicsdata, of the current epoch (e.g., current month).

As an example, for a driver who drove 1000 minutes during a previousepoch (e.g., last month) and is half way through a current epoch (e.g.,this month), the telematics server may presume that the driver woulddrive 500 more minutes in the current epoch (e.g., this month), and atthe same quality that the driver drove during the previous (or current)epoch. The telematics server may then determine the simulated or hybridepoch driver score for the current epoch (e.g., current month) based atleast in part upon a weighting or blending of the actual/previous epochdriver score from the previous epoch (e.g., last month) with thepredicted driver score of the current epoch, and further based at leastin part upon the elapsed time of the current epoch. As an example, if aprevious epoch included 1000 minutes, then where a driver has currentlydriven 500 minutes in the current epoch, 50% of the hybrid epoch driverscore would be based at least in part upon the previous epoch actualscore and 50% would be based at least in part upon the current epoch'ssimulated or predicted score.

In various embodiments, the systems and methods of the presentdisclosure provides a hybrid scoring approach that implements aniterative, epoch-based solution with numerous benefits, including thatall vehicle trips that are higher-quality than average (e.g., no hardbreaking, no speeding, no swerving, etc.), which would improve thehybrid epoch driver score (and vice versa). In addition, the hybridepoch driver score, through time, does not have sudden spikes or drops,and, therefore, provides a smooth estimate to the end-user (driver). Inaddition, the hybrid epoch driver score becomes, the actual score at theend of the month as the hybrid epoch driver score becomes, andcomputationally replaces, the next epochs previous epoch score. Thus,implementation of the hybrid scoring solution allows for a predictedscore to be reported to the user, which can also be the basis for apredicted or expected invoice to the user, allowing the cloud-basedvehicular telematics to solve the aforementioned problems concerninglack of certainty and variability with ongoing reporting, pricing,and/or otherwise determining future driving metrics for the benefit ofend-users (e.g., drivers).

In various examples, systems and methods of the present disclosure, viathe implementation of the hybrid scoring solution, allows the telematicsserver to conserve memory and computational resources by avoiding theneed for the telematics server to store and maintain previous scores,including across a potentially numerous and variable set of drivers orend-users. For the same reasons, this allows the telematics server to bea less robust, lighter weight, and/or less expensive, server orcomputing device. The vehicular telematics systems and methods describedherein may allow for lightweight and accurate predictions on smallerand/or cheaper computational devices. In certain examples,edge-computing may effectively transfer all data processing to thetelematics device instead of a server, such as by using online learningalgorithms that don't require large memory footprints to continuallyupdate the discount on the device. In certain examples, a score may beuploaded to an external server to be used as an input for calculating afinal bill of the user.

In some examples, an edge-computing method may keep a queue ofprocesses, such as by queuing multiple processes (e.g., calculations) tobe executed upon a triggering event, such as when the edge-device, suchas a mobile device, is switched to a charging mode. In some examples,raw sensor data from sensors associated with a vehicle of a user may bestored entirely on the edge-device. In certain examples, the raw sensordata are transformed (e.g., compressed) into transformed data (e.g., ofa different dimension), such as one or more vectors, which are thenstored onto the edge-device. In some examples, raw sensor data aretransformed (e.g., compressed) into descriptive data readable by humanto indicate one or more characteristics associated with vehicleoperation, which are then stored onto the edge-device. In variousexamples, telematics data and/or non-telematics data (e.g.,environmental data, traffic data), such as from a third-party device ora server, are used for edge-computing. As an example, a method ofedge-computing includes collecting and/or receiving telematics dataassociated with vehicle operation during one or more trips, calibratingthe telematics data based at least in part upon one or more operationalconditions associated with the one or more trips, requesting and/orreceiving supplemental data such as environmental data and/or trafficdata, and determining, using an edge-device, a hybrid epoch-driverscore. In some examples, data stored onto the edge-device may be backedup onto a server, such as automatically at a pre-determined time of day,week, month, or year, and/or manually activated by a user. In variousexamples, the edge-device is configured to maintain a live score (e.g.,a logged score or a main policy vector) that updates, such ascontinuously and/or incrementally during a period (e.g., an epoch),which is generated at the edge-device and stored at the edge-device. Thelive score may be used for determining a discount, such as a predicteddiscount, which may be shown in a display and presented to a user, suchas at the end of the period.

Advantages will become more apparent to those of ordinary skill in theart from the following descriptions of the embodiments which have beenshown and described by way of illustration. As will be realized, thepresent embodiments may be capable of other and different embodiments,and their details are capable of modification in various respects.Accordingly, the drawings and description are to be regarded asillustrative in nature and not as restrictive.

FIG. 1A represents an embodiment of telematics cloud platform 100configured to receive vehicular telematics data from a telematics deviceonboard a vehicle 108, which includes infrastructure, including hardwaredevices, as described for various telematics system and methodsembodiments herein. In particular, FIG. 1A illustrates a telematicsserver 110 configured to receive vehicular telematics data from one ormore telematics devices (e.g., telematics device 106 i and/or mobiledevice 106 m) onboard a vehicle 108. As the term is used herein,“telematics data” may include vehicle specific data, sensor data, and/orvehicle environment related data that is generated, collected,monitored, measured, transmitted and/or otherwise manipulated by one ormore telematics devices (e.g., telematics device 106 i and/or mobiledevice 106 m) or sensors associated with a vehicle. The telematics datamay include various metrics that indicate the direction, speed,acceleration, braking, cornering, and/or motion of the vehicle in whichthe data are associated. Such data may be determined from sensors (e.g.,sensors 118) on board the vehicle, a mobile telematics device travelingwith the vehicle, GPS systems, or other such device described herein.The telematics data may include geographic position information defininga geographic location of a telematics device associated with a vehicle.Such data may include latitude and longitude coordinates, for example.The telematics data may further include time value of the geographicposition information, defining a specific point in time the telematicsdevice was at a given geographic location.

In certain examples, telematics cloud platform 100 may include bothhardware and software components, where software components may executeon the hardware devices. Telematics cloud platform 100 may communicatevia various data communication channels for communicating data (e.g.,telematics data) between and among the various components.

As illustrated in FIG. 1A, telematics cloud platform 100 may besegmented into a set of front-end components 102 and a set of back-endcomponents 104. The front-end components 102 may include a vehicle 108which may be, in some examples, an automobile, a car, a truck, a towtruck, a snowplow, a boat, a motorcycle, a motorbike, a scooter, arecreational vehicle, or any other type of vehicle capable of roadway orwater travel. Telematics device 106 i may be permanently or removablyinstalled onboard vehicle 108, and may generally be an on-boardcomputing device capable of performing various functionalities relatingto vehicular telemetric data generation, collection, and/ortransmission. In some examples, telematics device 106 i may be anintegrated device of the vehicle. Further, telematics device 106 i maybe installed by the manufacturer of vehicle 108, or as an aftermarketmodification or addition to vehicle 108. In FIG. 1A, although only onetelematics device 106 i is depicted, it should be understood that insome embodiments, a plurality of computers telematics devices 106 i(which may be installed at one or more locations within vehicle 108) maybe used.

Telematics cloud platform 100 may further include mobile device 106 mthat may be associated with vehicle 108, where mobile device 106 m maybe any type of electronic device such as a smartphone, notebookcomputer, tablet, “phablet,” GPS (Global Positioning System) orGPS-enabled device, smart watch, smart glasses, smart bracelet, wearableelectronic, PDA (personal digital assistants), pager, computing deviceconfigured for wireless communication, and/or the like. Mobile device106 m may implement one or more mobile operation systems, such as APPLEIOS or GOOGLE ANDROID. Mobile device 106 m may be equipped or configuredwith a set of sensors, such as a location module (e.g., a GPS chip), animage sensor, an accelerometer, a clock, a gyroscope, a compass, a yawrate sensor, a tilt sensor, and/or other sensors.

Mobile device 106 m may belong to or be otherwise associated with a user(e.g., a driver of vehicle 108), where the user may be an owner ofvehicle 108 or otherwise associated with vehicle 108. In some examples,mobile device 106 m may be a mobile device of a user, where such mobiledevice performs any and/or all of a telematics device as describedherein. In some examples, the user may rent vehicle 108 for a variableor allotted time period, or the individual may at least partiallyoperate (or be a passenger of) vehicle 108 as part of a ride share. Insome examples, the user may at least partially operate vehicle 108 (andmay thus be an operator of the vehicle), or may be a passenger ofvehicle 108 (e.g., if vehicle 108 is an autonomous vehicle). Accordingto embodiments, a user may carry or otherwise have possession of mobiledevice 106 m during operation of vehicle 108, regardless of whether theindividual is the operator or passenger of vehicle 108.

In some embodiments, telematics device 106 i may operate in conjunctionwith mobile device 106 m to perform any or all of the functionsdescribed herein, including generating, collecting, and/or transmittingtelematics data as described herein. In certain embodiments, telematicsdevice 106 i may perform any or all of the on-board vehicle functionsdescribed herein, in which case mobile device 106 m may not be presentor may not be connected to telematics device 106 i. In still certainembodiments, mobile device 106 m may perform any or all of the onboardfunctions described herein.

Telematics device 106 i and/or mobile device 106 m may communicativelyinterface with one or more on-board sensors 118 that are disposed on orwithin vehicle 108 and that may be utilized to monitor vehicle 108 andthe environment in which vehicle 108 is operating. In particular, theone or more on-board sensors 118 may sense conditions associated withvehicle 108 and/or associated with the environment in which vehicle 108is operating, and may generate telematics data indicative of the sensedconditions. In some examples, the telematics data may include a locationand/or operation data indicative of operation of vehicle 108. In someconfigurations, at least some of the on-board sensors 118 may be fixedlydisposed at various locations on vehicle 108. In certain examples, atleast some of the on-board sensors 118 may be incorporated within orconnected to telematics device 106 i. In certain examples, at least someof the on-board sensors 118 may be included on or within mobile device106 m.

The on-board sensors 118 may communicate respective telematics data totelematics device 106 i and/or to mobile device 106 m, and thetelematics data may be processed using telematics device 106 i and/ormobile device 106 m to determine when vehicle 108 is in operation aswell as determine information regarding operation of vehicle 108. Insome situations, the on-board sensors 118 may communicate respectivetelematics data indicative of the environment in which vehicle 108 isoperating. In some examples, telematics device 106 i and/or mobiledevice 106 m may additionally be configured to obtain geographiclocation data and/or telematics data by communicating with sensors 118.In some embodiments, on-board computer may obtain geographic locationdata via communication with a vehicle-integrated global navigationsatellite system (GNSS), GPS, etc. To provide additional examples,on-board computer may obtain one or more metrics related to the speed,direction, and/or motion of vehicle 108 via any number of suitablesensors (e.g., sensors 118), which can include speedometer sensors,braking sensors, airbag deployment sensors, crash detection sensors,accelerometers, etc.

According to embodiments, the sensors 118 may include one or more of aGPS unit, a radar unit, a LIDAR unit, an ultrasonic sensor, an infraredsensor, some other type of electromagnetic energy sensor, a microphone(e.g., to support detect/listen for audio/sound wave of siren(s)associated with an emergency vehicle), a radio (e.g., to supportwireless emergency alerts or an emergency alert system), an inductancesensor, a camera, an accelerometer, an odometer, a system clock, agyroscope, a compass, a geo-location or geo-positioning unit, a locationtracking sensor, a proximity sensor, a tachometer, a speedometer, and/orthe like. Some of the on-board sensors 118 (e.g., GPS, accelerometer, ortachometer units) may provide telematics data indicative of, in someexamples, the vehicle's 108 location, speed, position acceleration,direction, responsiveness to controls, movement, etc.

Other sensors 118 may be directed to the interior or passengercompartment of vehicle 108, such as cameras, microphones, pressuresensors, weight sensors, thermometers, or similar sensors to monitor anypassengers, operations of instruments included in vehicle 108,operational behaviors of vehicle 108, and/or conditions within vehicle108. In some examples, on-board sensors 118 directed to the interior ofvehicle 108 may provide telematics data indicative of, in some examples,in-cabin temperatures, in-cabin noise levels, data from seat sensors(e.g., indicative of whether or not an individual is using a seat, andthus the number of passengers being transported by vehicle 108), datafrom seat belt sensors, data regarding the operations of user controlleddevices such as windshield wipers, defrosters, traction control, mirroradjustment, interactions with on-board user interfaces, etc.Additionally, the on-board sensors 118 may further detect and monitorthe health of the occupant(s) of vehicle 108 (e.g., blood pressure,heart rate, blood sugar, temperature, etc.).

In various embodiments of telematics cloud platform 100, front-endcomponents 102 may communicate collected telematics data to back-endcomponents 104 (e.g., via a network(s) 120). In particular, at least oneof telematics device 106 i or mobile device 106 m may communicate withback-end components 104 via the network(s) 120 to enable back-endcomponents 104 to receive and/or store collected telematics data andinformation regarding usage of vehicle 108.

The network(s) 120 may include a proprietary network, a secure publicinternet, a virtual private network, and/or some other type of network,such as dedicated access lines, plain ordinary telephone lines,satellite links, cellular data networks, combinations of these and/orother types of networks. The network(s) 120 may utilize one or moreradio frequency communication links to communicatively connect tovehicle 108, e.g., utilize wireless communication link(s) tocommunicatively connect with mobile device 106 m and telematics device106 i. Where the network(s) 120 comprises the Internet or other datapacket network, data communications may take place over the network(s)120 via an Internet or other suitable data packet communicationprotocol. In certain examples, the network(s) 120 includes one or morewired communication links or networks.

Back-end components 104 include one or more servers or computingdevices, which may be implemented as a server bank/server farm, or cloudcomputing platform, and is interchangeably referred to herein astelematics server 110. Telematics server 110 may include one or morecomputer processors adapted and configured to execute various softwareapplications and components of telematics cloud platform 100, inaddition to other software components, as described herein.

Telematics server 110 may further include or be communicativelyconnected to one or more data storage devices 132 (e.g., databases),which may be adapted to store telematics data related to the operationof vehicle 108, or GUI value data that is determined from telematicsdata, as described herein. In some examples, the one or more datastorage devices 132 may be implemented as a data bank or a cloud datastorage system, at least a portion of which may be locally accessed bytelematics server 110 using a local access mechanism such as a functioncall or database access mechanism (e.g., SQL), and/or at least a portionof which may be remotely accessed by telematics server 110 using aremote access mechanism such as a communication protocol. Telematicsserver 110 may access data stored in the one or more data storagedevices 132 when executing various functions and tasks associated withthe present disclosure, including, in some examples, receivingtelematics data from telematics device 106 i and/or mobile device 106 m,and/or transmitting GUI values to a telematics mobile app as describedherein.

Back-end components 104 may further include one or more remoteplatform(s) 112, which may be any system, entity, repository, or thelike, capable of obtaining and storing data, processing data, orreturning values or data associated with vehicle operation as describedherein. Although FIG. 1A depicts the set of remote platform(s) 112 asseparate from the one or more data storage devices 132, it should beappreciated that the set of remote platform(s) 112 may be included aspart of the one or more data storage devices 132. In some embodiments,the remote platform(s) 112 may store or process data indicative ofvehicle operation regulations. In some examples, the third-party source112 may store speed limit information, direction of travel information,lane information, map information, route information, and/or similarinformation. The remote platform(s) 112 may also maintain or obtainreal-time data indicative of traffic signals for roadways (e.g., whichtraffic signals currently have red lights or green lights). In certainexamples, the one or more data storage devices or entities 132 may storethe data indicative of vehicle operation regulations.

To communicate with telematics server 110 and other portions of back-endcomponents 104, front-end components 102 may include a communicationcomponent(s) 135, 136 that are configured to transmit information to andreceive information from back-end components 104. The communicationcomponents 135, 136 may include one or more wireless transmitters ortransceivers operating at any desired or suitable frequency orfrequencies.

Wireless transmitters or transceivers may operate at differentfrequencies and/or by using different protocols, if desired. In anexample, mobile device 106 m may include a respective communicationcomponent 136 for sending or receiving information to and fromtelematics server 110 via the network(s) 120, such as over one or moreradio frequency links or wireless communication channels which support afirst communication protocol (e.g., GSM, CDMA, LTE, one or more IEEE802.11 standards such as Wi-Fi, WiMAX, BLUETOOTH, etc.). In certainexamples, telematics device 106 i may operate in conjunction with anon-board transceiver or transmitter 135 that is disposed at vehicle 108(which may, in some examples, be fixedly attached to vehicle 108) forsending or receiving information to and from telematics server 110 viathe network(s) 120, such as over one or more radio frequency links orwireless communication channels which support the first communicationprotocol and/or a second communication protocol.

In some embodiments, telematics device 106 i may operate in conjunctionwith mobile device 106 m to utilize the communication component 136 ofmobile device 106 m to deliver telematics data to back-end components104. Similarly, telematics device 106 i may operate in conjunction withmobile device 106 m to utilize the communication component 135 ofvehicle 108 to deliver telematics data to back-end components 104. Insome embodiments, the communication components 135, 136 and theirrespective links may be utilized by telematics device 106 i and/ormobile device 106 m to communicate with back-end components 104.

Accordingly, either one or both of mobile device 106 m or telematicsdevice 106 i may communicate (e.g., send telematics data) via network(s)120 over the link(s). Additionally, in some configurations, mobiledevice 106 m and telematics device 106 i may communicate with oneanother directly over a wireless or wired link. Telematics device 106 iand/or mobile device 106 m disposed at vehicle 108 may communicate viathe network(s) 120 and the communication component(s) 135, 136 by usingone or more suitable wireless communication protocols (e.g., GSM, CDMA,LTE, one or more IEEE 802.11 Standards such as Wi-Fi, WiMAX, BLUETOOTH,etc.).

FIG. 1B illustrates a block diagram of telematics server 110 and atelematics device 106 (e.g., telematics device 106 i and/or mobiledevice 106 m) of FIG. 1A in accordance with various embodimentsdisclosed herein. In particular, FIG. 1B illustrates a hardware diagramof an example telematics device 106 (such as telematics device 106 iand/or mobile device 106 m as discussed with respect to FIG. 1A) and anexample telematics server 110 (e.g., telematics server 110 as discussedwith respect to FIG. 1A), in which the systems and methods as discussedherein may be implemented.

As shown in FIG. 1B, telematics device 106 may include a processor 172as well as a memory 178. Memory 178 may store an operating system 179capable of facilitating the functionalities as discussed herein as wellas a set of applications 175 (e.g., machine readable instructions). Insome examples, one of the set of applications 175 may be an analysisapplication 190 configured to facilitate several of the functionalitiesas discussed herein. It should be appreciated that one or more otherapplications 192 are envisioned, such as an application for generating,collecting, monitoring, measuring, and/or transmitting telematics datavia telematics device 106 as described herein.

Processor 172 may interface with the memory 178 to execute the operatingsystem 179 and the set of applications 175. According to someembodiments, the memory 178 may also include telematics data 180including data accessed or collected from a set of sensors (e.g.,sensors 118) or directly via a telematics device (e.g., telematicsdevice 106 i and/or mobile device 106 m). The memory 178 may include oneor more forms of volatile and/or non-volatile, fixed and/or removablememory, such as read-only memory (ROM), electronic programmableread-only memory (EPROM), random access memory (RAM), erasableelectronic programmable read-only memory (EEPROM), and/or other harddrives, flash memory, MicroSD cards, and others.

Telematics device 106 may further include a communication module 177configured to communicate data via one or more networks 120. Accordingto some embodiments, the communication module 177 may include one ormore transceivers (e.g., WWAN, WLAN, and/or WPAN transceivers)functioning in accordance with IEEE standards, 3GPP standards, or otherstandards, and configured to receive and transmit data via one or moreexternal ports 176. In some examples, the communication module 177 mayinterface with another device, component, or sensors via the network(s)120 to retrieve sensor data.

In some embodiments, telematics device 106 may include a set of sensors171 such as, in some examples, a location module (e.g., a GPS chip), animage sensor, an accelerometer, a clock, a gyroscope, a compass, a yawrate sensor, a tilt sensor, telematics sensors, and/or other sensors.Telematics device 106 may further include user interface 181 configuredto present information to a user and/or receive inputs from the user. Asshown in FIG. 1A, the user interface 181 may include a display screen182 and I/O components 164 (e.g., ports, capacitive or resistive touchsensitive input panels, keys, buttons, lights, LEDs). According to someembodiments, the user may access telematics device 106 via the userinterface 181 (e.g., a guided user interface (GUI)) to reviewinformation, make selections, and/or perform other functions.Additionally, telematics device 106 may include a speaker 173 configuredto output audio data and a microphone 174 configured to detect audio.

In some embodiments, telematics device 106 may perform thefunctionalities as discussed herein as part of a “cloud” network (e.g.,via network(s) 120 and telematics server 110) or may otherwisecommunicate with other hardware devices or software components withinthe cloud to send, retrieve, or otherwise analyze data. In someembodiments, telematics server 110 may operate as aSoftware-as-a-Service (SaaS) or Platform-as-a-Service (Paas), providingthe functionality of telematics server 110 remotely to software apps andother components in accordance with the various embodiments describedherein.

As illustrated in FIG. 1A and FIG. 1B, telematics device 106 maycommunicate and interface with telematics server 110 via the network(s)120. Telematics server 110 may include a processor 159 as well as amemory 156. The memory 156 may store an operating system 157 capable offacilitating the functionalities as discussed herein as well as a set ofcomponents 151 (e.g., machine readable instructions). In some examples,one of the set of components 151 may include epoch prediction component152 configured to facilitate several of the functionalities discussedherein. It should be appreciated that one or more other components 153are envisioned.

The processor 159 may interface with the memory 156 to execute theoperating system 157 and the set of components 151. According to someembodiments, the memory 156 may also include telematics data 158, suchas telematics data received from telematics device 106, and/or otherdata, other data as described herein. The memory 456 may include one ormore forms of volatile and/or non-volatile, fixed and/or removablememory, such as read-only memory (ROM), electronic programmableread-only memory (EPROM), random access memory (RAM), erasableelectronic programmable read-only memory (EEPROM), and/or other harddrives, flash memory, MicroSD cards, and others.

Telematics server 110 may further include a communication module 155configured to communicate data via the one or more networks 120.According to some embodiments, the communication module 155 may includeone or more transceivers (e.g., WWAN, WLAN, and/or WPAN transceivers)functioning in accordance with IEEE standards, 3GPP standards, or otherstandards, and configured to receive and transmit data via one or moreexternal ports 154. In some examples, the communication module 155 mayreceive, from telematics device 106, a set(s) of sensor data.

Telematics server 110 may further include user interface 162 configuredto present information to a user and/or receive inputs from the user. Asshown in FIG. 1A, the user interface 162 may include a display screen163 and I/O components 164 (e.g., ports, capacitive or resistive touchsensitive input panels, keys, buttons, lights, LEDs). According to someembodiments, the user may access telematics server 110 via the userinterface 162 to review information, make changes, input training data,and/or perform other functions.

In some embodiments, telematics server 110 may perform thefunctionalities as discussed herein as part of a “cloud” network or mayotherwise communicate with other hardware or software components withinthe cloud to send, retrieve, or otherwise analyze data.

In general, a computer program product in accordance with any embodimentmay include a computer usable storage medium (e.g., standard randomaccess memory (RAM), an optical disc, a universal serial bus (USB)drive, or the like) having computer-readable program code embodiedtherein, wherein the computer-readable program code may be adapted to beexecuted by the processors 172, 159 (e.g., working in connection withthe respective operating systems 179, 157) to facilitate the functionsas described herein. In this regard, the program code may be implementedin any desired language, and may be implemented as machine code,assembly code, byte code, interpretable source code or the like (e.g.,via Golang, Python, Scala, C, C++, Java, Actionscript, Objective-C,JavaScript, CSS, XML,). In some embodiments, the computer programproduct may be part of a cloud network of resources.

FIG. 2 illustrates a data transmission and implementation diagram of anexample cloud-based vehicular telematics system 200 for generatinghybrid epoch driver predictions in accordance with various embodimentsherein. Cloud-based vehicular telematics system 200 may include all, orpart, of the computing devices, features, and/or other functionality asdescribed herein for FIG. 1A and FIG. 1B. Accordingly, the disclosurefor FIG. 1A and FIG. 1B applies the same or similarly for FIG. 2 . Inparticular, cloud-based vehicular telematics system 200 includestelematics device 106 (e.g., telematics device 106 i and/or mobile 106m), telematics server 110, and remote platform 112, each as describedherein with respect to FIG. 1A and FIG. 1B.

In the embodiment of FIG. 2 , telematics device 106 generates and/orcollects (202) telematics data associated with operation of a vehicle(e.g., vehicle 108) during one or more vehicle trips of the vehicle.Telematics device 106 (e.g., telematics device 106 i and/or mobiledevice 106 m) may collect the telematics data, e.g., via sensors 118,GPS systems, or other systems or components as described herein for FIG.1A and FIG. 1B. In this way, vehicular telematics data, as describedherein, may define a vehicular trip of a vehicle (e.g., vehicle 108). Invarious embodiments, a plurality of vehicular telematics defining avehicular trip of a vehicle may define a telematics dataset having acertain data size (e.g., several gigabytes or megabytes of data). Thedata size is generally proportional to the number of telematics datarecords collected for the vehicle trip.

In some embodiments, the telematics data, as generated or collected bytelematics device 106, may include thousands, and in some instancesmillions or more, records of data. In some examples, in certainembodiments the telematics data may comprise 15 Hertz (Hz) data, such astelematics data that is generated or collected 15 times per second. Insuch embodiments, a trip with a duration of 10 minutes would result inthe generation and/or collection of 9,000 telematics data records.

A telematics data record, as the term is used herein, may refer to aninstance of vehicle or vehicle environment data determined at aparticular time. In some examples, in various embodiments herein, eachtelematics data record of a plurality of telematics data may include ageographic position of a telematics device (e.g., telematics device 106i and/or mobile device 106 m) and a time value of the geographicposition. In this way, telematics data are able to define a state ofvehicle and/or telematics device at a given point in time. As recorded,a telematics data record may comprise a single row of data as may berepresented in a data table, relational database, or other datastructure. In some embodiments, the telematics data may be associatedwith a particular user. In some examples, the telematics data may beassociated with a driver or passenger of the vehicle (e.g., vehicle108).

With respect to the embodiment of FIG. 2 , telematics device 106transmits (204) the telematics data (e.g., via network(s) 120) totelematics server 110. Telematics server 110 receives (e.g., via itsexternal ports and/or communication modules 155) and processes thetelematics data. Processing telematics data may refer to, but is notlimited to, generating hybrid epoch driver predictions as descriedherein, or performing pre-processing and/or post-processing analysis,such as determining start times for trip, positions, times, or otherdata or information as described herein, from the received telematicsdata. In some embodiments, processing telematics data may attachmetadata to the telematics data records, or GUI values, where suchmetadata include data generated, determined from, or otherwise resultingfrom the telematics data as received from telematics device 106.

In some embodiments, because of the large number of telematics datarecords generally received from telematics device 106, telematics server110 may receive (e.g., via its external ports and/or communicationmodules 155) and process the telematics data using data efficienttechniques. In some examples, telematics data, as received by telematicsdevice 106, is processed by telematics server 110 via an asynchronousprocess. In such embodiments, telematics server 110 may comprise aplurality of software components, including consumer components andproducer components. The consumer components are implemented to receiveand/or store (e.g., in memory 156 or data storage devices 132) incomingtelematics data. The producer components are implemented to process thetelematics data, in some examples, by compressing the telematics datainto GUI values, or performing post-processing analysis as describedherein.

In some embodiments, the consumer components and the producer componentsmay implement via multiple computational threads, in a multi-threadedenvironment of the telematics server 110. In a multi-threadedenvironment, consumer components and producer components may operate atthe same time so as to increase the throughput and efficiency of theprocessing of the telematics data as described herein.

Certain embodiments for receiving and processing telematics data arecontemplated herein, such that the telematics system and methods are notlimited to consumer/producer embodiments. In some examples, a singlesoftware component may be implemented for receiving and processing alltelematics data. In certain embodiments, telematics data could storetelematics data in memory (e.g., in memory 156 or data storage devices132), where a batch component of the telematics cloud form 110 couldselect certain amounts of telematics data to process at one time. Inthis way, such embodiments result in batch processing of receivedtelematics data. In such embodiments, telematics server 110 may beconfigured to batch process the telematics data at specific timeintervals (e.g., every minute, hour, day, etc.).

In still further embodiments, telematics data may be processed only whena request is received from a client device (e.g., mobile device of user)for the data. In some examples, a telematics mobile app executing on amobile device, as described herein, may be configured to request datafor a particular time period, which may cause the telematics server 110,e.g., via a client component, to process telematics data in real-time inorder to respond to the request. It should be appreciated that portionsof and/or combinations of any or all of the above embodiments may beused to process and receive telematics data as part of the telematicssystems and methods as described herein.

As described with respect to FIG. 1B, telematics server 110 may includean epoch prediction application or component (e.g., epoch predictioncomponent 152) configured to generate hybrid epoch driver predictions asdescribed herein. Epoch prediction component 152 is a softwareapplication, component, object, or set of objects, e.g., of anobject-oriented programming language, that implements predictiveanalytics with artificial intelligence, statistical analysis, or thelike using the driver telematics data as input, such as feature inputinto a machine learning algorithm. The epoch prediction component 152may receive telematics data (204) from telematics device 106implementing telematics mobile app during (or after the completion of)one or more vehicle trips. In some embodiments, the telematics data maybe pushed to the telematics server 110 from the telematics device 106and/or a telematics app. In certain embodiments, the telematics data maybe pulled by the telematics server, upon request by the telematicsserver, from the telematics device 106.

Epoch prediction component 152, as implemented on the telematics server110, is configured to execute instructions causing the telematics serverto implement one or more algorithms for generating real-time, nearreal-time, or trip-based driver epoch-based vehicle or driverpredictions. Epoch prediction component 152, as implemented on thetelematics server 110, may generate a previous epoch score (206) for aprevious epoch (e.g., a previous month, week, etc.) from telematics datadefined over one or more actual vehicle trips (e.g., of vehicle 108) ofthe driver during the previous epoch. As described herein, the previousepoch score may relate to, or be used to generate, a rate or discountassociated with a product or service (e.g., a rate or discountassociated with an insurance policy of the driver).

In addition, epoch prediction component 152, as implemented on thetelematics server 110, may generate a predicted epoch driver score (208)for a current epoch (e.g., a current month, week, etc.). Epochprediction component 152 determines the predicted for a remaining(future) time period of the current epoch. In various embodiments, epochprediction component 152 determines the predicted epoch driver scorefrom each of (1) telematics data (204) defined over one or more actualvehicle trips of the driver during the current epoch, and (2) andtelematics data predicted, such as via machine learning, over one ormore expected vehicle trips of the driver during the current epoch.Epoch prediction component 152 may predict one or more expected vehicletrips (or total time value for such trips) for the driver of vehicle 108for the remaining (future) current epoch time (e.g., the period of timefor the current epoch that has not yet elapsed). The expected set ofvehicle trips may be determined based at least in part upon the driver'spast driving behavior as determined from past telematics data (e.g.,from telematics data of one or more previous epochs). In variousembodiments, both quantity and/or quality metrics of the user's pastdriving behavior may be generated using artificial intelligence orsimilar techniques, including statistical or regression analysis,machine-learning, natural networks, etc.

The epoch prediction component 152 may include a machine learning modelused to predict or generate predicted epoch driver scores. Such machinelearning model may be trained using a supervised or unsupervised machinelearning program or algorithm. The machine learning program or algorithmmay employ a neural network, which may be a convolutional neuralnetwork, a deep learning neural network, or a combined learning moduleor program that learns in two or more features or feature datasets in aparticular areas of interest. The machine learning programs oralgorithms may also include natural language processing, semanticanalysis, automatic reasoning, regression analysis, support vectormachine (SVM) analysis, decision tree analysis, random forest analysis,K-Nearest neighbor analysis, naïve Bayes analysis, clustering,reinforcement learning, and/or other machine learning algorithms and/ortechniques. Machine learning may involve identifying and recognizingpatterns in existing data (e.g., the quality or quantity of driving of adriver for a specific epoch as determined from the telematics data of acurrent or previous epoch) in order to facilitate making predictions forsubsequent data (e.g., to predict how a driver will drive during aremaining time period of a current or future epoch).

Machine learning model(s), such as those of epoch prediction component152, may be created and trained based at least in part upon example(e.g., “training data,”) inputs or data (which may be termed “features”and “labels”) in order to make valid and reliable predictions for newinputs, such as testing level or production level data or inputs. Insupervised machine learning, a machine learning program operating on aserver, computing device, or otherwise processor(s), may be providedwith example inputs (e.g., “features”) and their associated, orobserved, outputs (e.g., “labels”) in order for the machine learningprogram or algorithm to determine or discover rules, relationships, orotherwise machine learning “models” that map such inputs (e.g.,“features”) to the outputs (e.g., labels), in some examples, bydetermining and/or assigning weights or other metrics to the modelacross its various feature categories. Such rules, relationships, orotherwise models may then be provided subsequent inputs in order for themodel, executing on the server, computing device, or otherwiseprocessor(s), to predict, based at least in part upon the discoveredrules, relationships, or model, an expected output.

In unsupervised machine learning, the server, computing device, orotherwise processor(s), may be needed to find its own structure inunlabeled example inputs, where, for example multiple trainingiterations are executed by the server, computing device, or otherwiseprocessor(s) to train multiple generations of models until asatisfactory model, e.g., a model that provides sufficient predictionaccuracy when given test level or production level data or inputs, isgenerated. The disclosures herein may use one or both of such supervisedor unsupervised machine learning techniques.

In FIG. 2 , epoch prediction component 152 may use a driver's telematicsdata (e.g., telematics data 204) as features to train the epochprediction component 152's model against labels that may include drivingscores, such as past driving scores that the user achieved for pastepochs having the same or similar quantity and/or quality of telematicsdata a currently received. Quality of the telematics data may refer tothe degree to which the driver demonstrated safe or acceptable drivingbehavior (or vice versa) as defined by the telematics data. In someexamples, metrics such as hard breaking, speeding, swerving, and thelike may be determined from the data. Quantity of the telematics datamay refer to an amount of driving time as defined by the telematicsdata. The output of epoch prediction component 152 may be a predictedepoch driver score that defines an expected driver score for the driverat the end of the current epoch using any of the quality or quantitydata as feature input values.

Telematics server 110 may generate the hybrid epoch driver score (210)from portions of each of the previous epoch driver score and a predicteddriver epoch score. In various embodiments, telematics server 110 maygenerate hybrid epoch driver score (210) as a blended, weighted, orotherwise hybrid score using each of the previous epoch driver score(206) and a predicted driver epoch score (208). In some examples, aproportionate predicted (future) epoch driver score is determined forthe current epoch and is weighted or proportioned according to theremaining current epoch time. Similarly, a proportionate previous epochdriver score is determined for the previous epoch and is also weightedor proportioned according to the remaining current epoch time. A greaterremaining current epoch time may cause a greater weighting or proportionof the previous epoch driver score to influence the hybrid epoch driverscore. In contrast, a lesser remaining current epoch time may cause alesser weighting or proportion of the previous epoch driver score toinfluence the hybrid epoch driver score.

Hybrid epoch driver scores may be implemented by the telematics server110 from portions of each of the previous epoch driver score and apredicted driver epoch score using various algorithms. In a firstestimation algorithm, telematics server 110 determines a percentage oftime (p) that has elapsed for a current epoch (e.g., a current month, acurrent billing period, etc.). In some examples, if a quarter of thecurrent epoch has already elapsed (e.g., a quarter of a billing periodhas elapsed), then p would equal 0.25. In such embodiment, telematicsserver 110 determines an estimated minutes of driving for the remainingcurrent epoch time. In particular, telematics server 110 multiplies(1−p) times the number of minutes driven by a driver in a previous epoch(e.g., in a previous month, billing cycle, etc.). If no previous epochsare available (because the driver is new), then telematics server 110may assume a standard or starting number of minutes (which may bedetermined from other driver records). In some examples, a new drivermay be presumed to have driven 45 hours in a previous epoch based atleast in part upon similarly situated drivers. The telematics server 110may then add the actual minutes driven by the driver in the currentepoch. In some examples, if the telematics server 110 collected 1000minutes for the driver in the previous epoch and the driver has driven400 minutes in the current epoch period, then, if the percentage of time(p) that has elapsed for the current epoch period (p) is 0.25, then anestimated total minutes of driving for the current epoch is(1−0.25)*1000+400=1150 minutes. For the first estimation algorithm, thisvalue is the predicted epoch driver score. The telematics server 110 maythen determine the hybrid epoch driver score using the predicted epochdriver score, the estimated driving time, and the actual driving time.In particular, the hybrid epoch driver score may be determined as theweighted average of the previous epoch driver score and the predictedepoch driver score based at least in part upon the percentage of time(p) formulation. In some examples, if the predicted epoch driver scoreis 0.35 (e.g., based at least in part upon quality and quantity ofdriving) and the previous epoch driver score is 0.20, and a quarter ofthe month has passed (p=0.25), then telematics server 110 determines thehybrid epoch driver score asp*predicted_epoch_driver_score+(1−p)*previous epoch driverscore=0.25*0.35+0.75*0.20=0.2375. The hybrid epoch driver score may beupdated in real-time, near real-time, after completion of a trip, or atsome other time period (such as once a day). In this way, the nextprevious epoch driver score will equal, or become equal to, thepredicted epoch driver score, as the current epoch ends and a newcurrent epoch begins. In this way, the driver's hybrid epoch driverscore is affected by two main activities, as determined via the driver'stelematics data, namely, the quantity and quality of the driver'sdriving habits.

As a further example, a sub-score algorithm may be used to generate ahybrid epoch driver score. In sub-score algorithm, a set value of tripminutes (e.g., 900 minutes) is determined or set for a driver for thecurrent epoch by the telematics server 110. Each of the minutes of theset value of trip minutes is assigned a sub-score level based at leastin part upon the prior epoch's sub-score levels. In the sub-scorealgorithm, at the beginning of each epoch time period, telematics server110 sets a driver's current sub-scores equal to his or her prior epoch'ssub-scores. If the driver is new (e.g., a first epoch for the driver),then the driver's current sub-scores is determined based at least inpart upon sub-scores of other drivers, such as by using an average,median, etc. of other similarly situated or otherwise drivers. In thesub-score algorithm, once the driver takes a first trip, the driver'ssub-scores are updated based at least in part upon the duration andquality of the trip. In some examples, if the first trip was 30 minutes,then 870 parts (reflective of minutes) of the sub-score would be basedat least in part upon a previous epoch's sub-scores (e.g., this is theprevious epoch driver score) and 30 parts of the sub-score displayedwould be based at least in part upon the current epoch's (e.g., thecurrent month's) actual performance (e.g., this is the current epochdriver score). In this way, as the driver takes additional trips duringthe current epoch, the sub-scores continue to be more and moredetermined based at least in part upon the driver's actual driving(e.g., what driving has occurred in the current epoch versus theprevious epoch). In scenarios where drivers do not drive the set valueof trip minutes (e.g., 900 minutes), telematics server 110 would blend aproportional amount of actual driving the driver actually engaged in forthe current epoch. Such drivers would likely experience positive hybridepoch driver scores regardless of how they drove (e.g., quality ofdriving) due to getting nearly positive results for time-based drivingfactors (e.g., quantity of driving). Additionally, drivers whosesub-scores were similar to what they were in the previous epoch (e.g.,previous month) would see no impact regardless of how many minutes theydrove in the current epoch (e.g., current month).

The sub-score algorithm provides an accurate model, especially when thedriver has driven the set value of tip minutes (e.g., 900 minutes). Eachtrip may cause the telematics server 110 to change the driver'ssub-score (as described above), which can lead to low variability of thesub-scores. In some examples, at the beginning of an epoch (e.g.,beginning of a month), a driver's sub-score would be stable. Anadditional benefit of the sub-score algorithm is that the driversub-scores are comparable. That is, any driver can compare his or hersub-score with any other driver such that the comparison would beone-to-one (for users having the same set value of trip minutes). Insome examples, a user with a braking score of 85 may be determined as abetter driver at braking than a driver with a braking score of 75 (givena measurement of braking on a 0 to 100 scale). Further, in the sub-scorealgorithm, trips with high quality driving may cause sub-scores toincrease. In contrast, trips with low quality driving trips may causethe sub-scores to decrease. This provides for a reliable and repeatable(and intuitive to the end-user/driver) approach to determining a hybridepoch driver score.

As a further example, a driver-focused scoring algorithm may be used togenerate a hybrid epoch driver score. In the driver-focused scoringalgorithm, telematics server 110 predicts a driver score of a driver fora current epoch based at least in part upon age, expected drivingquality, expected driving quantity, number of vehicles, vehicle type,and/or other possible factors. In some examples, presume a driver is 35years old, has one vehicle, and, as determined from telematics data ofprevious epochs, drives 1800 minutes (e.g. one hour per day for an epochperiod of one month). Given only the driver's previous trip minutes perepoch (e.g., 1800 in this example) and the above presumptions,telematics server 110 may determine the predicted epoch driver score ofthe driver for the current epoch (e.g., the current epoch). Thetelematics server 110 may then determine, from the driver's telematicsdata, the number of minutes (n_minutes) the driver has driven for thecurrent epoch (e.g., current month). The telematics server 110 may alsodetermine, from the driver's past epoch telematics data, a number ofdriving minutes that the driver typically drives in a typical month (t).In some examples, t can equal 900 minutes if the driver usually drives900 minutes in an epoch (e.g., past months). From the above, a hybridepoch driver score may be determined for the driver by telematics server110, where t equals 900, by determining(n_minutes/900)*predicted_epoch_driver_score+((900−n_minutes)/900)*previous_epoch_driver_score.In this way, the driver-focused scoring algorithm will cause thetelematics server 110 to determine a hybrid epoch driver score with abetter value indicating a higher quality driver with higher qualitytrips, and vice-versa.

With the driver-focused scoring algorithm, hybrid epoch driver scoresare not impacted by the quantity of driving. Instead, the score is onlyimpacted based at least in part upon how the person drives (e.g.,quality of driving), which means that if several days pass and no tripsare logged (via telematics data as described herein), then a person'sdriving score will not change. In some examples, if a driver's initial,presumed, or predicted driving epoch score is 50 (on a scale from 0 to100), but the telematics server 110 later determines, from thetelematics data, that the driver's actual driving ability is 10, thenhow quickly the driver's score drops to 10 is based at least in partupon how often the driver drives (because the driver-focused scoringalgorithm needs trips/telematics data to update). On the other hand, ifthe driver drives frequently (e.g., drives 900 minutes in the first weekof the month, where t equals 900), then the initial score of 50 would nolonger even factor into telematics server 110's determination. Inaddition, with the driver-focused scoring algorithm, the hybrid epochdriver score remains stable. In some examples, a single trip has only asmall impact on a driver's score. Rarely would a driver score increaseor decrease by several points as a result of any single long trip. Inthis way, with the driver-focused scoring algorithm, a driver is alwayscompeting with his or her performance in the prior epoch (e.g., priormonth). If the person drives better (e.g., higher quality driving) thanlast epoch (e.g., last month), the driver's hybrid epoch driver scorewill go up. Such information can be reported to the driver in real-time,near real-time, or after the completion of vehicle trip(s) as describedherein.

In some embodiments, it may be determined that additional data areneeded to generate the predicted driver epoch score (209 a). Such datamay include additional feature data for training a machine learningmodel as described herein. Such determination may be made when there isinsufficient data to generate an accurate prediction. In any event, suchadditional data may be combined with the telematics data collected bymobile device 106 m in order to generate the hybrid epoch driverpredictions or other predictions described herein. In some examples,telematics data of other drivers, as stored or collect by remoteplatforms and/or third parties (e.g., remote platform 112) may bedownloaded or otherwise received by telematics server 110 (209 b).

Telematics server 110 may transmit to a telematics mobile application(app) 201 the hybrid epoch driver score (212) to report the score to thedriver (or another end-user). The transmission may be made in real-timeor near real-time as the driver operates during, and/or completes,vehicle trips. The telematics mobile application 201 may be configuredto display the hybrid epoch driver score on the display via the GUI. Inaddition, the telematics mobile application may render or generate aguided user interface (GUI) on a display of the telematics device. TheGUI may include one or more screens, including GUI control screens orcontrols, to allow for manipulation of the telematics mobile applicationby a user, such as by a driver of one or more vehicles of the one ormore vehicle trips.

In various embodiments, telematics mobile app 201 may be a downloadableapplication that executes on a mobile device phone (e.g., mobile device106 m) or another personal electronic device. In some examples,telematics mobile app 201 collect or cause to collect, telematics dataas described herein. In some embodiments, during a collection period,telematics mobile app 201 does may not need a personal electronic device(e.g., mobile device 106 m) to be mounted or otherwise affixed to thevehicle (e.g., vehicle 108). To this end, telematics mobile app 201enables mobile device 106 m and/or telematics server 110 to isolate themotion of the vehicle from the motion of the personal electronic device,as may be determined by the telematics data, via each respective device.As a result, telematics mobile app 201 is able to meaningfully interpretcollected sensor or telematics data regardless of the particularorientation of mobile device 106 m.

As described herein, telematics mobile app 201 may collect and transmitthe telematics/sensor data to telematics server 110. Telematics server110 is configured to analyze the telematics/sensor data to determine orgenerate information regarding the quality (e.g., how the vehicle wasdriven) and/or quantity (e.g., how much the vehicle was drive) of thedata. In some examples, telematics server 110 may identify periods oftime indicative of mobile device 106 m being located within a vehicle inmotion. As another example, the telematics server 110 may analyze thetelematics/sensor data generated during the identified periods todetermine driving performance, such as acceleration, braking, and/orcornering performance (e.g., quality of driving). Said another way,after the telematics data are received (204), telematics server 110 mayprocess the telematics/sensor data collected during a particular drivingperiod or trip to derive driving information.

In various embodiments, telematics server 110 may implement arepresentative state transfer (RESTful) application programminginterface (API) that exposes the telematics data and/or driver scores tobe pushed and/or pulled via telematics mobile app 201 as descriedherein. In some examples, telematics mobile app 201 may access thehybrid epoch driver score, and/or related data, by pulling or requestingsuch data from the RESTful API. In certain embodiments, telematicsmobile app 201 may access hybrid epoch driver scores, and/or relatedata, by receiving pushed GUI values from the RESTful API, where aconnection, channel or session was established between telematics mobileapp 201 and telematics server 110.

In some embodiments, the telematics mobile app 201 may leverage an APIof the operating system executing on the personal electronic device todetect when the personal electronic device is in motion. In someexamples, telematics mobile app 201 may utilize the API to wake mobiledevice 106 m in response to the operating system detecting a locationchange. To this end, the operating system may detect the major orsignificant location change by determining that the position of themobile device 106 m has moved a threshold distance or if a base station,in communication with, and servicing the mobile device 106 m changes.Upon receiving an indication that the operating system detected themajor location change, the telematics mobile app 201 may begincollecting, recording, and/or transmitting sensor or telematics datagenerated by sensors of the mobile device 106 m or other sensors asdescribed herein. Telematics mobile app 201 may then wait until mobiledevice 106 m has been stationary for a threshold amount of time beforethe telematics mobile app 201 stops collecting sensor/telematics data.

In some embodiments, the operating system of mobile device 106 m, via athird party or other API, may automatically report when the motion ofthe mobile device 106 m is indicative of the mobile device 106 m beinglocated within a vehicle (e.g., vehicle 108) in motion (as opposed tobeing carried by an end user that is walking or biking). Upon detectingan API event indicating that the mobile device 106 m is within a vehiclein motion, the telematics mobile app 201 may begin collectingtelematics/sensor data generated by sensors of mobile device 106 m.During a driving period, the telematics mobile app 201 may collecttelematics/sensor data generated by various sensors of the mobile device106 m, or other sensors as describes herein, and may store the collectedsensor data in application memory and/or transmits it to telematicsserver 110.

Additionally, in still further embodiments, the operating system, via athird party or other API, may also automatically report when the motionof mobile device 106 m is indicative of mobile device 106 m no longerbegin within the vehicle in motion. Accordingly, in such embodiments,telematics mobile app 201 may cease recording the sensor data upondetecting the API event indicating that mobile device 106 m is no longerwithin a vehicle in motion.

FIG. 3 illustrates telematics mobile app 201 rendering a guided userinterface (GUI) 301 on a display of the telematics device 106 of FIG. 1Aand FIG. 1B in accordance with various embodiments disclosed herein. Inthe embodiment of FIG. 3 , telematics mobile app 201 executes on amobile device (e.g., mobile device 106 m), such as a mobile smartphoneas described herein. In the embodiment of FIG. 3 , telematics mobile app201 uses a hybrid epoch driver score, as received from telematics server110, to determine a driver discount amount 306 (e.g., “$16.82”). Thedriver discount is then used to determine a projected reward 304 (e.g.,“$21.82”). As described herein, the hybrid epoch driver score (and hencethe values of the driver discount and projected reward) are based atleast in part upon each of the previous epoch driver score and apredicted driver epoch score, as determined and provided by telematicsserver 110. As described herein, each of these values may each changewith the amount of remaining time in the current epoch and the driver'sactual driving activity, which may cause a change in the predicteddriver epoch score (and, thus, the hybrid epoch driver score).Accordingly, the driver discount 306 and projected reward 304, asdisplayed by telematics mobile app 201, may each fluctuate and/or changein real-time, near-time, or on a per-trip-basis as each of the previousepoch driver score and a predicted driver epoch score are accessed,determined, generated, and/or regenerated by the telematics server 110,and provided to telematics mobile app 201. In some examples, in theembodiment of FIG. 3 , telematics mobile app 201 shows a completed tripon Friday, September 22 of a distance of 5.3 miles. This trip may havecaused the hybrid epoch driver score to be recomputed, transmitted tomobile device 106, and then rendered and displayed (e.g., as driverdiscount 306 and projected reward 304) as described herein.

FIG. 4 illustrates a flow diagram of a telematics method 400 forgenerating hybrid epoch driver predictions in accordance with variousembodiments disclosed herein. At block 402, a telematics mobileapplication (e.g., telematics mobile application 201) executes on atelematics device (e.g., mobile device 106 m) to collect telematics datafrom one or more sensors during one or more vehicle trips.

At block 404, a telematics server (e.g., telematics server 110) receivesthe telematics data from the telematics mobile application (e.g.,telematics mobile application 201) during the one or more vehicle trips.

At block 406, the telematics server (e.g., telematics server 110)executes instructions causing the telematics server to generate ordetermine a previous epoch score of a previous epoch (e.g., a previousmonth, week, etc.) from telematics data defined over one or more actualvehicle trips of a driver during the previous epoch. As describedherein, in some embodiments, the previous epoch score may relate to, orbe used to generate, a rate or discount associated with a product orservice (e.g., a rate or discount associated with an insurance policy ofthe driver), for example as described for FIG. 3 .

At block 408, the telematics server (e.g., telematics server 110)executes instructions causing the telematics server to generate ordetermine a predicted epoch driver score for a current epoch (e.g., acurrent month, week, etc.). In method 400, the telematics serverdetermines the predicted epoch driver score based at least in part upona remaining (future) time period of the current epoch. In particular, inmethod 400, the predicted epoch driver score is determined from each of(1) telematics data defined over one or more actual vehicle trips of thedriver during the current epoch, and (2) and telematics data predictedover one or more expected vehicle trips of the driver during the currentepoch. In some examples, one or more expected vehicle trips (or totaltime value for such trips) may be predicted for the driver for theremaining (future) current epoch time (e.g., the period of time for thecurrent epoch that has not yet elapsed). The expected set of vehicletrips may be determined based at least in part upon the driver's pastdriving behavior as determined from past telematics data (e.g., fromtelematics data of one or more previous epochs). In various embodiments,both quantity and/or quality metrics of the user's past driving behaviormay be generated using artificial intelligence or similar techniques,including statistical or regression analysis, machine-learning, naturalnetworks, etc. as described herein.

At block 410, telematics server (e.g., telematics server 110) executesinstructions causing the telematics server to generate a hybrid epochdriver score from portions of each of the previous epoch driver scoreand a predicted driver epoch score. In various embodiments, a blended,weighted, or otherwise hybrid score is generated by one or morealgorithms using each of the previous epoch driver score and a predicteddriver epoch score. In some examples, the proportionate predicted(future) epoch driver score is determined for the current epoch and isweighted or proportioned according to the remaining current epoch time.Similarly, a proportionate previous epoch driver score is determined forthe previous epoch and is also weighted or proportioned according to theremaining current epoch time. A greater remaining current epoch time maycause a greater weighting or proportion of the previous epoch driverscore to influence the hybrid epoch driver score. In contrast, a lesserremaining current epoch time may cause a lesser weighting or proportionof the previous epoch driver score to influence the hybrid epoch driverscore.

At block 412, the telematics server (e.g., telematics server 110)transmits to the telematics mobile application (e.g., telematics mobileapplication 201) the hybrid epoch driver score. The transmission may bemade in real-time or near real-time as the driver operates during,and/or completes, vehicle trips. The telematics mobile application maybe configured to display the hybrid epoch driver score on the displayvia the GUI, as shown and described, in some examples, for FIG. 3 .

With the foregoing, a user of the above telematics systems and methodswho is an insurance customer or user may opt-in to rewards, insurancediscount, or other type of program. After the insurance customerprovides their permission or affirmative consent, an insurance providertelematics application and/or remote server may collect telematicsand/or other data (including image or audio data) associated withinsured assets, including before, during, and/or after aninsurance-related event or vehicle accident, such as any event, etc., asmay be determined from the vehicular telematics data, GUI values,environment data, vehicle status data, or other information or data asdescribed herein. In return, risk adverse drivers, and/or vehicle ownersmay receive discounts or insurance cost savings related to auto, home,life, and other types of insurance from the insurance provider.

In certain examples, a general risk score is calculated based at leastin part upon telematics data, which may be similar (e.g., in effect) toa credit score, such as a score indicative of risk associated with aparticular user. For example, a method of creating a general risk scoreincludes training a model, such as a model that is configured tosimultaneously predict a multitude of risk scores across a diverse setof use cases. The training may include training a neural network thathas a hidden layer with a single hidden node and additional layers beingused to predict the multitude of risk scores (e.g., multi-targetprediction). In some examples, the general risk score may be a vector ofscores instead of a single score, such as by expanding the size of thehidden layer with a single node to be a hidden layer with multiplenodes. In certain examples, having multitude of scores allows forgreater resolution in unique scoring attributes. While a neural networkmay be used, other models for predicting each task may also be used,such as using principal component analysis or a similar technique on thevector of predictions to find the n components that best explain thevariance of the full-set of predictions.

In one aspect, telematics data, and/or other data, including the typesof data discussed elsewhere herein, may be collected or received by aninsured's mobile device or smart vehicle, a Telematics Applicationrunning thereon, and/or an insurance provider remote server, such as viadirect or indirect wireless communication or data transmission from aTelematics Application (“App”) running on the insured's mobile device orsmart vehicle, after the insured or customer affirmatively consents orotherwise opts-in to an insurance discount, reward, or other program.The insurance provider may then analyze the data received with thecustomer's permission to provide benefits to the customer. As a result,risk adverse customers may receive insurance discounts or otherinsurance cost savings based at least in part upon data that reflectslow risk driving behavior and/or technology that mitigates or preventsrisk to (i) insured assets, such as vehicles or even homes, and/or (ii)vehicle operators or passengers.

Additional aspects include a telematics cloud platform receivingtelematics data and/or geographic location data from a large number ofmobile computing devices (e.g., 100 or more), and issuing alerts tothose mobile computing devices in which the alerts are relevant inaccordance with the various techniques described herein.

One or More Systems for Generating a Hybrid Epoch Score UsingEdge-Computing According to Various Embodiments

FIG. 5 is a simplified diagram showing a system 500 for generating ahybrid epoch score using edge-computing, according to variousembodiments of the present disclosure. This diagram is merely anexample, which should not unduly limit the scope of the claims. One ofordinary skill in the art would recognize many variations, alternatives,and modifications. In some examples, the system 500 includes a dataretrieving module 502, a data collecting module 504, and an epoch scoregenerating module 506. In certain examples, the system 500 is configuredto implement method 600 of FIG. 6 . Although the above has been shownusing a selected group of components, there can be many alternatives,modifications, and variations. In some examples, some of the componentsmay be expanded and/or combined. Some components may be removed. Othercomponents may be inserted to those noted above. Depending upon theembodiment, the arrangement of components may be interchanged withothers replaced.

In various embodiments, the data retrieving module 502 is configured toretrieve (e.g., pull and/or receive), such as from an edge-computingdevice associated with the user and/or from a server, prior telematicsdata indicative of the operation of a vehicle by the user during one ormore prior trips in a prior epoch.

In various embodiments, the data collecting module 504 is configured tocollect, such as using one or more sensors of the edge-computing device,recent telematics data indicative of the operation of the vehicle by theuser during one or more recent trips in a current epoch.

In various embodiments, the epoch score generating module 506 isconfigured to generate, using the edge-computing device, a prior epochscore based at least in part upon the prior telematics data. In variousexamples, the epoch score generating module 506 is configured togenerate, using the edge-computing device, a partial current epoch scorebased at least in part upon the recent telematics data. In variousexamples, the epoch score generating module 506 is configured togenerate, using the edge-computing device, a hybrid current epoch scorebased at least in part upon the prior epoch score and the partialcurrent epoch score.

In some examples, the prior epoch is associated with a prior epoch time.In some examples, the one or more recent trips is associated with arecent driving time and an elapsed time. In certain examples, the recentdriving time is the amount of time the vehicle was under operation inthe current epoch, and/or the elapsed time is the amount of time thathas passed in the current epoch. In some embodiments, the epoch scoregenerating module 506 is configured to generate, using theedge-computing device, the hybrid current epoch score by at least:determining, using the edge-computing device, a remaining time of thecurrent epoch as the prior epoch time minus the elapsed time, anddetermining, using the edge-computing device, the hybrid current epoch.In various examples, the hybrid current epoch is determined, using theedge-computing device, based at least in part upon adding the priorepoch score multiplied by the remaining time and the partial currentepoch score multiplied by the recent driving time. For example, thehybrid current epoch may equal to the addition of the prior epoch scoremultiplied by the remaining time and the partial current epoch scoremultiplied by the recent driving time. Note that a remaining time, adriving time, and/or an elapsed time may be a time ratio of an epochtime (e.g., of a prior epoch or of a current epoch).

In some examples, the current epoch is associated with a plurality oftime segments. In various examples, each recent trip of the plurality ofrecent trips is associated with one or more time segments of theplurality of time segments. In some embodiments, the epoch scoregenerating module 506 is configured to generate, using theedge-computing device, the partial current epoch score by at least:assigning, using the edge-computing device, each segment of theplurality of time segments with the prior epoch score, generating, foreach recent trip of the plurality of recent trips, a partial currentepoch score based at least in part upon the recent telematics data; andupdating, for each recent trip of the plurality of recent trips usingthe edge-computing device, the associated one or more time segments withthe associated partial current epoch score. In some embodiments, theepoch score generating module 506 is configured to generate, using theedge-computing device, the hybrid current epoch score based at least inpart upon: the partial current epoch score for each segment of theplurality of time segments that was updated; and the prior epoch scorefor each segment of the plurality of time segments that was not updated.For example, the hybrid current epoch score may be generated, using theedge-computing device, as equal to an average epoch score across allsegments of the plurality of time segments.

In some embodiments, the data retrieving module 502 is furtherconfigured to retrieve (e.g., pull and/or receive), such as from theedge-computing device and/or a server, historic telematics dataindicative of the operation of the vehicle by the user during one ormore historic trips in one or more historic epochs. In various examples,the one or more recent trips is associated with a recent driving timeand an elapsed time, the recent driving time being the amount of timethe vehicle was under operation in the current epoch, and/or the elapsedtime being the amount of time that has passed in the current epoch. Insome embodiments, the epoch score generating module 506 is configured todetermine, using the edge-computing device, a historic epoch time basedat least in part upon the historic telematics data, the historic epochtime being the average total time of a historic epoch of the one or morehistoric epochs. In some embodiments, the epoch score generating module506 is configured to generate, using the edge-computing device, thehybrid current epoch score by at least: determining, using theedge-computing device, a remaining time of the current epoch as thehistoric epoch time minus the elapsed time; and determining, using theedge-computing device, the hybrid current epoch score based at least inpart upon adding: the partial current epoch score multiplied by a ratioof the elapsed time to a time historically driven per epoch and theprior epoch score multiplied by a ratio of the remaining time to thetime historically driven per epoch. For example, the hybrid currentepoch score may be determined, using the edge-computing device, as equalto the addition of the partial current epoch score multiplied by theratio of the elapsed time to the time historically driven per epoch andthe prior epoch score multiplied by the ratio of the remaining time tothe time historically driven per epoch.

In certain embodiments, the system 500 is configured to back uptelematics data to an external server, such as when the edge-computingdevice enters a charging mode and/or at a pre-determined time (e.g., endof trip, a time of day, a time of week, or a time of epoch).

In certain embodiments, the system 500 is configured to transformtelematics data collected by the one or more sensors associated with theedge-computing device into one or more vectors to be stored on a memoryof the edge-computing device with reduced size.

One or More Methods for Generating a Hybrid Epoch Score UsingEdge-Computing According to Various Embodiments

FIG. 6 is a simplified method for generating a hybrid epoch score usingedge-computing, according to various embodiments of the presentdisclosure. This diagram is merely an example, which should not undulylimit the scope of the claims. One of ordinary skill in the art wouldrecognize many variations, alternatives, and modifications. The method600 includes a process 602 of retrieving, such as from an edge-computingdevice and/or a server, prior telematics data indicative of theoperation of a vehicle by the user during one or more prior trips in aprior epoch, a process 604 of collecting, such as using one or moresensors of the edge-computing device, recent telematics data indicativeof the operation of the vehicle by the user during one or more recenttrips in a current epoch, a process 606 of generating, using theedge-computing device, a prior epoch score based at least in part uponthe prior telematics data, a process 608 of generating, using theedge-computing device, a partial current epoch score based at least inpart upon the recent telematics data, and a process 610 of generating,using the edge-computing device, a hybrid current epoch score based atleast in part upon the prior epoch score and the partial current epochscore. In certain examples, the method 600 is configured to beimplemented by system 500 of FIG. 5 . Although the above has been shownusing a selected group of processes for the method, there can be manyalternatives, modifications, and variations. In some examples, some ofthe processes may be expanded and/or combined. Other processes may beinserted to those noted above. Depending upon the embodiment, thesequence of processes may be interchanged with others replaced. In someexamples, some or all processes of the method are performed by acomputing device or a processor directed by instructions stored inmemory. As an example, some or all processes of the method are performedaccording to instructions stored in a non-transitory computer-readablemedium.

In some examples, the prior epoch is associated with a prior epoch time.In some examples, the one or more recent trips is associated with arecent driving time and an elapsed time. In some examples, the recentdriving time being the amount of time the vehicle was under operation inthe current epoch, and/or the elapsed time being the amount of time thathas passed in the current epoch. In some embodiments, the process 610 ofgenerating a hybrid current epoch score includes: determining, using theedge-computing device, a remaining time of the current epoch as theprior epoch time minus the elapsed time; and determining, using theedge-computing device, the hybrid current epoch based at least in partupon adding: the prior epoch score multiplied by the remaining time andthe partial current epoch score multiplied by the recent driving time.For example, determining the hybrid current epoch includes determining,using the edge-computing device, the hybrid current epoch to be equal tothe addition of the prior epoch score multiplied by the remaining timeand the partial current epoch score multiplied by the recent drivingtime.

In some examples, the current epoch is associated with a plurality oftime segments. In some examples, each recent trip of the plurality ofrecent trips is associated with one or more time segments of theplurality of time segments. In some embodiments, the process 608 ofgenerating the partial current epoch score includes: assigning, usingthe edge-computing device, each segment of the plurality of timesegments with the prior epoch score; generating, for each recent trip ofthe plurality of recent trips using the edge-computing device, a partialcurrent epoch score based at least in part upon the recent telematicsdata; and updating, for each recent trip of the plurality of recenttrips using the edge-computing device, the associated one or more timesegments with the associated partial current epoch score. In someembodiments, the process 610 of generating the hybrid current epochscore includes generating, using the edge-computing device, the hybridcurrent epoch based at least in part upon: the partial current epochscore for each segment of the plurality of time segments that wasupdated; and the prior epoch score for each segment of the plurality oftime segments that was not updated. For example, determining the hybridcurrent epoch includes determining, using the edge-computing device, thehybrid current epoch to be equal to an average epoch score across allsegments of the plurality of time segments.

In some embodiments, the method 600 further includes retrieving, such asfrom the edge-computing device and/or a server, historic telematics dataindicative of the operation of the vehicle by the user during one ormore historic trips in one or more historic epochs. In some embodiments,the method 600 further includes determining, using the edge-computingdevice, a historic epoch time based at least in part upon the historictelematics data, the historic epoch time being the average total time ofa historic epoch of the one or more historic epochs. In variousexamples, the one or more recent trips is associated with a recentdriving time and an elapsed time, the recent driving time being theamount of time the vehicle was under operation in the current epoch,and/or the elapsed time being the amount of time that has passed in thecurrent epoch. In some embodiments, the process 610 of generating thehybrid current epoch score includes: determining, using theedge-computing device, a remaining time of the current epoch as thehistoric epoch time minus the elapsed time; and determining, using theedge-computing device, the hybrid current epoch based at least in partupon adding: the partial current epoch score multiplied by a ratio ofthe elapsed time to a time historically driven per epoch; and the priorepoch score multiplied by a ratio of the remaining time to the timehistorically driven per epoch. For example, determining the hybridcurrent epoch includes determining, using the edge-computing device, thehybrid current epoch to be equal to the addition of the partial currentepoch score multiplied by the ratio of the elapsed time to the timehistorically driven per epoch and the prior epoch score multiplied bythe ratio of the remaining time to the time historically driven perepoch.

In certain embodiments, the method 600 further includes backing uptelematics data to an external server, such as when the edge-computingdevice enters a charging mode and/or at a pre-determined time (e.g., endof trip, a time of day, a time of week, or a time of epoch).

In certain embodiments, the method 600 further includes transformingtelematics data collected by the one or more sensors associated with theedge-computing device into one or more vectors to be stored on a memoryof the edge-computing device with reduced size.

One or More Computer Devices According to Various Embodiments

FIG. 7 is a simplified diagram showing a computer device 5000, accordingto various embodiments of the present disclosure. This diagram is merelyan example, which should not unduly limit the scope of the claims. Oneof ordinary skill in the art would recognize many variations,alternatives, and modifications. In some examples, the computer device5000 includes a processing unit 5002, a memory unit 5004, an input unit5006, an output unit 5008, and a communication unit 5010. In variousexamples, the computer device 5000 is configured to be in communicationwith a user 5100 and/or a storage device 5200. In certain examples, thesystem computer device 5000 is configured according to system 500 ofFIG. 5 , and/or to implement method 600 of FIG. 6 . Although the abovehas been shown using a selected group of components, there can be manyalternatives, modifications, and variations. In some examples, some ofthe components may be expanded and/or combined. Some components may beremoved. Other components may be inserted to those noted above.Depending upon the embodiment, the arrangement of components may beinterchanged with others replaced.

In various embodiments, the processing unit 5002 is configured forexecuting instructions, such as instructions to implement method 600 ofFIG. 6 . In some embodiments, executable instructions may be stored inthe memory unit 5004. In some examples, the processing unit 5002includes one or more processing units (e.g., in a multi-coreconfiguration). In certain examples, the processing unit 5002 includesand/or is communicatively coupled to one or more modules forimplementing the systems and methods described in the presentdisclosure. In some examples, the processing unit 5002 is configured toexecute instructions within one or more operating systems, such as UNIX,LINUX, Microsoft Windows®, etc. In certain examples, upon initiation ofa computer-implemented method, one or more instructions is executedduring initialization. In some examples, one or more operations isexecuted to perform one or more processes described herein. In certainexamples, an operation may be general or specific to a particularprogramming language (e.g., C, C #, C++, Java, or other suitableprogramming languages, etc.). In various examples, the processing unit5002 is configured to be operatively coupled to the storage device 5200,such as via an on-board storage unit 5012.

In various embodiments, the memory unit 5004 includes a device allowinginformation, such as executable instructions and/or other data to bestored and retrieved. In some examples, memory unit 5004 includes one ormore computer readable media. In some embodiments, stored in memory unit5004 include computer readable instructions for providing a userinterface, such as to the user 5004, via the output unit 5008. In someexamples, a user interface includes a web browser and/or a clientapplication. In various examples, a web browser enables one or moreusers, such as the user 5004, to display and/or interact with mediaand/or other information embedded on a web page and/or a website. Incertain examples, the memory unit 5004 include computer readableinstructions for receiving and processing an input, such as from theuser 5004, via the input unit 5006. In certain examples, the memory unit5004 includes random access memory (RAM) such as dynamic RAM (DRAM) orstatic RAM (SRAM), read-only memory (ROM), erasable programmableread-only memory (EPROM), electrically erasable programmable read-onlymemory (EEPROM), and/or non-volatile RAM (NVRAN).

In various embodiments, the input unit 5006 is configured to receiveinput, such as from the user 5004. In some examples, the input unit 5006includes a keyboard, a pointing device, a mouse, a stylus, a touchsensitive panel (e.g., a touch pad or a touch screen), a gyroscope, anaccelerometer, a position detector (e.g., a Global Positioning System),and/or an audio input device. In certain examples, the input unit 5006,such as a touch screen of the input unit, is configured to function asboth the input unit and the output unit.

In various embodiments, the output unit 5008 includes a media outputunit configured to present information to the user 5004. In someembodiments, the output unit 5008 includes any component capable ofconveying information to the user 5004. In certain embodiments, theoutput unit 5008 includes an output adapter, such as a video adapterand/or an audio adapter. In various examples, the output unit 5008, suchas an output adapter of the output unit, is operatively coupled to theprocessing unit 5002 and/or operatively coupled to an presenting deviceconfigured to present the information to the user, such as via a visualdisplay device (e.g., a liquid crystal display (LCD), a light emittingdiode (LED) display, an organic light emitting diode (OLED) display, acathode ray tube (CRT) display, an “electronic ink” display, a projecteddisplay, etc.) or an audio display device (e.g., a speaker arrangementor headphones).

In various embodiments, the communication unit 5010 is configured to becommunicatively coupled to a remote device. In some examples, thecommunication unit 5010 includes a wired network adapter, a wirelessnetwork adapter, a wireless data transceiver for use with a mobile phonenetwork (e.g., Global System for Mobile communications (GSM), 3G, 4G, orBluetooth), and/or other mobile data networks (e.g., WorldwideInteroperability for Microwave Access (WIMAX)). In certain examples,other types of short-range or long-range networks may be used. In someexamples, the communication unit 5010 is configured to provide emailintegration for communicating data between a server and one or moreclients.

In various embodiments, the storage unit 5012 is configured to enablecommunication between the computer device 5000, such as via theprocessing unit 5002, and an external storage device 5200. In someexamples, the storage unit 5012 is a storage interface. In certainexamples, the storage interface is any component capable of providingthe processing unit 5002 with access to the storage device 5200. Invarious examples, the storage unit 5012 includes an Advanced TechnologyAttachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small ComputerSystem Interface (SCSI) adapter, a RAID controller, a SAN adapter, anetwork adapter, and/or any other component capable of providing theprocessing unit 5002 with access to the storage device 5200.

In some examples, the storage device 5200 includes any computer-operatedhardware suitable for storing and/or retrieving data. In certainexamples, storage device 5200 is integrated in the computer device 5000.In some examples, the storage device 5200 includes a database, such as alocal database or a cloud database. In certain examples, the storagedevice 5200 includes one or more hard disk drives. In various examples,the storage device is external and is configured to be accessed by aplurality of server systems. In certain examples, the storage deviceincludes multiple storage units such as hard disks or solid state disksin a redundant array of inexpensive disks (RAID) configuration. In someexamples, the storage device 5200 includes a storage area network (SAN)and/or a network attached storage (NAS) system.

One or More Computer Systems According to Various Embodiments

FIG. 8 is a simplified computer system 7000 according to variousembodiments of the present disclosure. This diagram is merely anexample, which should not unduly limit the scope of the claims. One ofordinary skill in the art would recognize many variations, alternatives,and modifications. In some examples, the system 7000 includes a vehiclesystem 7002, a network 7004, and a server 7006. In certain examples, thesystem 7000, the vehicle system 7002, and/or the server 7006 isconfigured according to system 500 of FIG. 5 , and/or to implementmethod 600 of FIG. 6 . Although the above has been shown using aselected group of components, there can be many alternatives,modifications, and variations. In some examples, some of the componentsmay be expanded and/or combined. Some components may be removed. Othercomponents may be inserted to those noted above. Depending upon theembodiment, the arrangement of components may be interchanged withothers replaced.

In various embodiments, the vehicle system 7002 includes a vehicle 7010and a client device 7012 associated with the vehicle 7010. In variousexamples, the client device 7012 is an on-board computer embedded orlocated in the vehicle 7010. As an example, the client device 7012 is amobile device (e.g., a smartphone) that is connected (e.g., via a wiredconnection or a wireless connection) to the vehicle 7010. In someexamples, the client device 7012 includes a processor 7016 (e.g., acentral processing unit (CPU), and/or a graphics processing unit (GPU)),a memory 7018 (e.g., storage unit, random-access memory (RAM), and/orread-only memory (ROM), flash memory), a communications unit 7020 (e.g.,a network transceiver), a display unit 7022 (e.g., a touchscreen), andone or more sensors 7024 (e.g., an accelerometer, a gyroscope, amagnetometer, and/or a GPS sensor).

In various embodiments, the vehicle 7010 is operated by a user. Incertain embodiments, the system 7000 includes multiple vehicles 7010,each vehicle of the multiple vehicles operated by a respective user ofmultiple users. In various examples, the one or more sensors 7024monitors, during one or more vehicle trips, the vehicle 7010 by at leastcollecting data associated with one or more operating parameters of thevehicle, such as speed, acceleration, braking, location, engine status,and/or other suitable parameters. In certain examples, the collecteddata include vehicle telematics data. According to some embodiments, thedata are collected continuously, at predetermined time intervals, and/orbased on one or more triggering events (e.g., when a sensor has acquiredmeasurements greater than a threshold amount of sensor measurements). Invarious examples, the data collected by the one or more sensors 7024correspond to user driving data, which may correspond to a driver'sdriving behaviors, in the methods and/or systems of the presentdisclosure.

According to various embodiments, the collected data are stored in thememory 7018 before being transmitted to the server 7006 using thecommunications unit 7020 via the network 7004 (e.g., via a local areanetwork (LAN), a wide area network (WAN), or the Internet). In someexamples, the collected data are transmitted directly to the server 7006via the network 7004. In certain examples, the collected data aretransmitted to the server 7006 via a third party. In some examples, adata monitoring system, managed or operated by a third party, isconfigured to store data collected by the one or more sensors 7024 andto transmit such data to the server 7006 via the network 7004 or adifferent network.

According to various embodiments, the server 7006 includes a processor7030 (e.g., a microprocessor, a microcontroller), a memory 7032 (e.g., astorage unit), a communications unit 7034 (e.g., a network transceiver),and a data storage 7036 (e.g., one or more databases). In some examples,the server 7006 is a single server, while in certain embodiments, theserver 7006 includes a plurality of servers with distributed processingand/or storage. In certain examples, the data storage 7036 is part ofthe server 7006, such as coupled via a network (e.g., the network 7004).In some examples, data, such as processed data and/or results, may betransmitted from the data storage, such as via the communications unit7034, the network 7004, and/or the communications unit 7020, to theclient device 7012, such as for display by the display 7022.

In some examples, the server 7006 includes various software applicationsstored in the memory 7032 and executable by the processor 7030. In someexamples, these software applications include specific programs,routines, and/or scripts for performing functions associated with themethods of the present disclosure. In certain examples, the softwareapplications include general-purpose software applications for dataprocessing, network communication, database management, web serveroperation, and/or other functions typically performed by a server. Invarious examples, the server 7006 is configured to receive, such as viathe network 7004 and via the communications unit 7034, the datacollected by the one or more sensors 7024 from the client device 7012,and stores the data in the data storage 7036. In some examples, theserver 7006 is further configured to process, via the processor 7030,the data to perform one or more processes of the methods of the presentdisclosure.

One or More Examples of Machine Learning According to VariousEmbodiments

According to some embodiments, a processor or a processing element maybe trained using supervised machine learning and/or unsupervised machinelearning, and the machine learning may employ an artificial neuralnetwork, which, in some examples, may be a convolutional neural network,a recurrent neural network, a deep learning neural network, areinforcement learning module or program, or a combined learning moduleor program that learns in two or more fields or areas of interest.Machine learning may involve identifying and recognizing patterns inexisting data in order to facilitate making predictions for subsequentdata. Models may be created based upon example inputs in order to makevalid and reliable predictions for novel inputs. In various examples,one or more processes of the methods of the present disclosure areperformed by one or more machine learning models, such as one or moreneural networks. In various examples, one or more modules of the systemsof the present disclosure perform actions by one or more machinelearning models, such as one or more neural networks.

According to certain embodiments, machine learning programs may betrained by inputting sample data sets or certain data into the programs,such as images, object statistics and information, historical estimates,and/or actual repair costs. The machine learning programs may utilizedeep learning algorithms that may be primarily focused on patternrecognition and may be trained after processing multiple examples. Themachine learning programs may include Bayesian Program Learning (BPL),voice recognition and synthesis, image or object recognition, opticalcharacter recognition, and/or natural language processing. The machinelearning programs may also include natural language processing, semanticanalysis, automatic reasoning, and/or other types of machine learning.

According to some embodiments, supervised machine learning techniquesand/or unsupervised machine learning techniques may be used. Insupervised machine learning, a processing element may be provided withexample inputs and their associated outputs and may seek to discover ageneral rule that maps inputs to outputs, so that when subsequent novelinputs are provided the processing element may, based upon thediscovered rule, accurately predict the correct output. In unsupervisedmachine learning, the processing element may need to find its ownstructure in unlabeled example inputs.

Examples of Certain Embodiments of the Present Disclosure

According to various embodiments, a computer-implemented method forgenerating a hybrid epoch score for a user using edge-computingincludes: retrieving, from an edge-computing device and/or a server,prior telematics data indicative of the operation of a vehicle by theuser during one or more prior trips in a prior epoch; collecting, usingone or more sensors of the edge-computing device, recent telematics dataindicative of the operation of the vehicle by the user during one ormore recent trips in a current epoch; generating, using theedge-computing device, a prior epoch score based at least in part uponthe prior telematics data; generating, using the edge-computing device,a partial current epoch score based at least in part upon the recenttelematics data; and generating, using the edge-computing device, ahybrid current epoch score based at least in part upon the prior epochscore and the partial current epoch score. In various examples, thecomputer-implemented method is implemented accordingly to method 600 ofFIG. 6 and/or is implemented by system 500 of FIG. 5 .

According to some embodiments, the prior epoch is associated with aprior epoch time; the one or more recent trips is associated with arecent driving time and an elapsed time, the recent driving time beingthe amount of time the vehicle was under operation in the current epoch,the elapsed time being the amount of time that has passed in the currentepoch. In various examples, the generating the hybrid current epochscore includes: determining, using the edge-computing device, aremaining time of the current epoch as the prior epoch time minus theelapsed time; and determining, using the edge-computing device, thehybrid current epoch score based at least in part upon adding: the priorepoch score multiplied by the remaining time; and the partial currentepoch score multiplied by the recent driving time.

According to some embodiments, the current epoch is associated with aplurality of time segments; each recent trip of the plurality of recenttrips is associated with one or more time segments of the plurality oftime segments. In various examples, the generating the partial currentepoch score includes: assigning, using the edge-computing device, eachsegment of the plurality of time segments with the prior epoch score;generating, for each recent trip of the plurality of recent trips usingthe edge-computing device, a partial current epoch score based at leastin part upon the recent telematics data; and updating, for each recenttrip of the plurality of recent trips using the edge-computing device,the associated one or more time segments with the associated partialcurrent epoch score. In various examples, the generating the hybridcurrent epoch score includes generating, using the edge-computingdevice, the hybrid current epoch score based at least in part upon: thepartial current epoch score for each segment of the plurality of timesegments that was updated; and the prior epoch score for each segment ofthe plurality of time segments that was not updated.

According to some embodiments, the computer-implemented method furtherincludes: retrieving, from the edge-computing device and/or the server,historic telematics data indicative of the operation of the vehicle bythe user during one or more historic trips in one or more historicepochs; and determining, using the edge-computing device, a historicepoch time based at least in part upon the historic telematics data, thehistoric epoch time being the average total time of a historic epoch ofthe one or more historic epochs. In various examples, the one or morerecent trips is associated with a recent driving time and an elapsedtime, the recent driving time being the amount of time the vehicle wasunder operation in the current epoch, and the elapsed time being theamount of time that has passed in the current epoch. In variousexamples, the generating the hybrid current epoch score includes:determining, using the edge-computing device, a remaining time of thecurrent epoch as the historic epoch time minus the elapsed time; anddetermining, using the edge-computing device, the hybrid current epochscore based at least in part upon adding: the partial current epochscore multiplied by a ratio of the elapsed time to a time historicallydriven per epoch; and the prior epoch score multiplied by a ratio of theremaining time to the time historically driven per epoch.

According to some embodiments, the prior telematics data include: priorqualitative data indicative of one or more prior driving behaviors ofthe user during the one or more prior trips; and prior quantitative dataindicative of a prior driving time in the prior epoch; the recenttelematics data include: recent qualitative data indicative of one ormore recent driving behaviors of the user during the one or more recenttrips; and recent quantitative data indicative of a recent driving timein the current epoch. In various examples, the generating the priorepoch score includes generating, using the edge-computing device, theprior epoch score based at least in part upon the prior qualitative dataand the prior quantitative data. In various examples, the generating thepartial current epoch score includes generating, using theedge-computing device, the partial current epoch score based at least inpart upon the recent qualitative data and the recent quantitative data.In various examples, the generating the hybrid current epoch scoreincludes generating, using the edge-computing device, the hybrid currentepoch score based at least in part upon the prior qualitative data, theprior quantitative data, the recent qualitative data, and the recentquantitative data.

According to some embodiments, the collecting the recent telematics dataincludes collecting, using one or more sensors of the edge-computingdevice, the recent telematics data in real-time, in near real-time,and/or continuously. In some examples, the generating the partialcurrent epoch score includes generating, using the edge-computingdevice, the partial current epoch score in real-time, in near real-time,and/or continuously. In some examples, the generating the hybrid currentepoch score includes generating, using the edge-computing device, thehybrid current epoch score in real-time, in near real-time, and/orcontinuously.

According to some embodiments, the computer-implemented method furtherincludes: retrieving third-party information associated with the one ormore prior trips and/or the one or more recent trips. In some examples,generating the prior epoch score includes generating, using theedge-computing device, the prior epoch score based at least in part uponthe prior telematics data and the third-party information. In someexamples, generating the partial current epoch score includesgenerating, using the edge-computing device, the partial current epochscore based at least in part upon the recent telematics data and thethird-party information.

According to some embodiments, the computer-implemented method furtherincludes: presenting the hybrid current epoch score to the user via adisplay associated with the edge-computing device.

According to some embodiments, the computer-implemented method furtherincludes: determining, using the edge-computing device, a rate ordiscount for an associated product or an associated service based atleast in part upon the hybrid current epoch score; and presenting therate or discount to the user via a display associated with theedge-computing device.

According to some embodiments, the computer-implemented method furtherincludes: determining, using the edge-computing device, a comparisonresult by at least comparing the hybrid current epoch score of the userto hybrid current epoch scores of one or more related users; andpresenting the comparison result to the user via a display associatedwith the edge-computing device.

According to some embodiments, the computer-implemented method furtherincludes: detecting, using the one or more sensors associated with theedge-computing device, whether the vehicle is being operated by theuser; and collecting, in response to detecting that the vehicle is beingoperated by the user, telematics data using the one or more sensors.

According to some embodiments, generating the prior epoch score includesgenerating, using a neural network of the edge-computing device, theprior epoch score based at least in part upon the prior telematics data.In various examples, generating the partial current epoch score includesgenerating, using a neural network of the edge-computing device, thepartial current epoch score based at least in part upon the recenttelematics data. In various examples, generating the hybrid currentepoch score includes generating, using a neural network of theedge-computing device, the hybrid current epoch score based at least inpart upon the prior epoch score and the partial current epoch score.

According to various embodiments, a system for generating a hybrid epochscore for a user includes: a data retrieving module configured to:retrieve, from an edge-computing device and/or a server, priortelematics data indicative of the operation of a vehicle by the userduring one or more prior trips in a prior epoch; a data collectingmodule configured to: collect, using one or more sensors of theedge-computing device, recent telematics data indicative of theoperation of the vehicle by the user during one or more recent trips ina current epoch; and an epoch score generating module configured to:generate, using the edge-computing device, a prior epoch score based atleast in part upon the prior telematics data; generate, using theedge-computing device, a partial current epoch score based at least inpart upon the recent telematics data; and generate, using theedge-computing device, a hybrid current epoch score based at least inpart upon the prior epoch score and the partial current epoch score. Invarious examples, the system is configured according to system 500 ofFIG. 5 and/or configured to perform method 600 of FIG. 6 .

According to some embodiments, the prior epoch is associated with aprior epoch time; the one or more recent trips is associated with arecent driving time and an elapsed time, the recent driving time beingthe amount of time the vehicle was under operation in the current epoch,the elapsed time being the amount of time that has passed in the currentepoch. In various examples, the epoch score generating module isconfigured to generate the hybrid current epoch score by at least:determining, using the edge-computing device, a remaining time of thecurrent epoch as the prior epoch time minus the elapsed time; anddetermining, using the edge-computing device, the hybrid current epochscore based at least in part upon adding: the prior epoch scoremultiplied by the remaining time; and the partial current epoch scoremultiplied by the recent driving time.

According to some embodiments, the current epoch is associated with aplurality of time segments; each recent trip of the plurality of recenttrips is associated with one or more time segments of the plurality oftime segments. In various examples, the epoch score generating module isconfigured to: generate the partial current epoch score by at least:assigning, using the edge-computing device, each segment of theplurality of time segments with the prior epoch score; generating, foreach recent trip of the plurality of recent trips using theedge-computing device, a partial current epoch score based at least inpart upon the recent telematics data; and updating, for each recent tripof the plurality of recent trips using the edge-computing device, theassociated one or more time segments with the associated partial currentepoch score; and generate, using the edge-computing device, the hybridcurrent epoch score based at least in part upon: the partial currentepoch score for each segment of the plurality of time segments that wasupdated; and the prior epoch score for each segment of the plurality oftime segments that was not updated.

According to some embodiments, the data retrieving module is furtherconfigured to retrieve, from the edge-computing device and/or theserver, historic telematics data indicative of the operation of thevehicle by the user during one or more historic trips in one or morehistoric epochs. In some examples, the one or more recent trips isassociated with a recent driving time and an elapsed time, the recentdriving time being the amount of time the vehicle was under operation inthe current epoch, the elapsed time being the amount of time that haspassed in the current epoch. In various examples, the epoch scoregenerating module is configured to: determine, using the edge-computingdevice, a historic epoch time based at least in part upon the historictelematics data, the historic epoch time being the average total time ofa historic epoch of the one or more historic epochs; generate, using theedge-computing device, the hybrid current epoch score by at least:determining, using the edge-computing device, a remaining time of thecurrent epoch as the historic epoch time minus the elapsed time; anddetermining, using the edge-computing device, the hybrid current epochscore based at least in part upon adding: the partial current epochscore multiplied by a ratio of the elapsed time to a time historicallydriven per epoch; and the prior epoch score multiplied by a ratio of theremaining time to the time historically driven per epoch.

According to some embodiments, the prior telematics data include: priorqualitative data indicative of one or more prior driving behaviors ofthe user during the one or more prior trips; and prior quantitative dataindicative of a prior driving time in the prior epoch; the recenttelematics data include: recent qualitative data indicative of one ormore recent driving behaviors of the user during the one or more recenttrips; and recent quantitative data indicative of a recent driving timein the current epoch. In various examples, the epoch score generatingmodule is configured to: generate, using the edge-computing device, theprior epoch score based at least in part upon the prior qualitative dataand the prior quantitative data; generate, using the edge-computingdevice, the partial current epoch score based at least in part upon therecent qualitative data and the recent quantitative data. In variousexamples, the epoch score generating module is configured to generate,using the edge-computing device, the hybrid current epoch score based atleast in part upon the prior qualitative data, the prior quantitativedata, the recent qualitative data, and the recent quantitative data.

According to some embodiments, the data retrieving module is configuredto retrieve the recent telematics data in real-time, in near real-time,and/or, continuously. In some examples, the epoch score generatingmodule is configured to generate, using the edge-computing device, thepartial current epoch score in real-time, in near real-time, and/or,continuously. In some examples, the epoch score generating module isconfigured to generate, using the edge-computing device, the hybridcurrent epoch score in real-time, in near real-time, and/or,continuously.

According to some embodiments, the data retrieving module is furtherconfigured to retrieve third-party information associated with the oneor more prior trips and/or the one or more recent trips. In someexamples, the epoch score generating module is configured to generate,using the edge-computing device, the prior epoch score based at least inpart upon the prior telematics data and the third-party information. Insome examples, the epoch score generating module is configured togenerate, using the edge-computing device, the partial current epochscore based at least in part upon the recent telematics data and thethird-party information.

According to some embodiments, the system further includes a presentingmodule configured to present the hybrid current epoch score to the uservia a display associated with the edge-computing device.

According to some embodiments, the system further includes a ratedetermining module configured to determine, using the edge-computingdevice, a rate or discount for an associated product or an associatedservice based at least in part upon the hybrid epoch score. In someexamples, the system further includes a presenting module configured topresent the rate or discount to the user via a display associated withthe edge-computing device.

According to some embodiments, the system further includes a comparisonmodule configured to determine, using the edge-computing device, acomparison result by at least comparing the hybrid epoch score of theuser to hybrid epoch scores of one or more related users. In someexamples, the system further includes a presenting module configured topresent the comparison result to the user via a display associated withthe edge-computing device.

According to some embodiments, the system further includes a detectingmodule configured to detect, using one or more sensors associated withthe edge-computing device, whether the vehicle is being operated by theuser. In some examples, the system further includes a collecting moduleconfigured to collect, in response to detecting that the vehicle isbeing operated by the user, telematics data using the one or moresensors.

According to some embodiments, the epoch score generating module isconfigured to: generate the prior epoch score, using a neural network ofthe edge-computing device, based at least in part upon the priortelematics data; generate, using a neural network of the edge-computingdevice, the partial current epoch score based at least in part upon therecent telematics data; and/or generate, using a neural network of theedge-computing device, the hybrid current epoch score based at least inpart upon the prior epoch score and the partial current epoch score.

According to various embodiments, a non-transitory computer-readablemedium with instructions stored thereon, that upon execution by aprocessor, causes the processor to perform: retrieving, from anedge-computing device and/or a server, prior telematics data indicativeof the operation of a vehicle by the user during one or more prior tripsin a prior epoch; collecting, using one or more sensors of theedge-computing device, recent telematics data indicative of theoperation of the vehicle by the user during one or more recent trips ina current epoch; generating, using the edge-computing device, a priorepoch score based at least in part upon the prior telematics data;generating, using the edge-computing device, a partial current epochscore based at least in part upon the recent telematics data; andgenerating, using the edge-computing device, a hybrid current epochscore based at least in part upon the prior epoch score and the partialcurrent epoch score. In various examples, the non-transitorycomputer-readable medium with instructions stored thereon, that uponexecution by the processor, perform one or more processes according tomethod 600 of FIG. 6 .

According to some embodiments, the non-transitory computer-readablemedium, that upon execution of the processor, further causes theprocessor to perform: retrieving, from the edge-computing device and/orthe server, historic telematics data indicative of the operation of thevehicle by the user during one or more historic trips in one or morehistoric epochs; and determining, using the edge-computing device, ahistoric epoch time based at least in part upon the historic telematicsdata, the historic epoch time being the average total time of a historicepoch of the one or more historic epochs; wherein: the one or morerecent trips is associated with a recent driving time and an elapsedtime, the recent driving time being the amount of time the vehicle wasunder operation in the current epoch, the elapsed time being the amountof time that has passed in the current epoch; the generating the hybridcurrent epoch score includes: determining, using the edge-computingdevice, a remaining time of the current epoch as the historic epoch timeminus the elapsed time; and determining, using the edge-computingdevice, the hybrid current epoch score based at least in part uponadding: the partial current epoch score multiplied by a ratio of theelapsed time to a time historically driven per epoch; and the priorepoch score multiplied by a ratio of the remaining time to the timehistorically driven per epoch.

According to some embodiments, the non-transitory computer-readablemedium, that upon execution of the processor, further causes theprocessor to perform: retrieve third-party information associated withthe one or more prior trips and/or the one or more recent trips. In someexamples, generating the prior epoch score includes: generating, usingthe edge-computing device, the prior epoch score based at least in partupon the prior telematics data and the third-party information; and/orgenerating the partial current epoch score includes generating, usingthe edge-computing device, the partial current epoch score based atleast in part upon the recent telematics data and the third-partyinformation.

According to some embodiments, the non-transitory computer-readablemedium, that upon execution of the processor, further causes theprocessor to perform: presenting the hybrid current epoch score to theuser via a display associated with the edge-computing device.

According to some embodiments, the non-transitory computer-readablemedium, that upon execution of the processor, further causes theprocessor to perform: determining, using the edge-computing device, arate or discount for an associated product or an associated servicebased at least in part upon the hybrid epoch score; and presenting therate or discount to the user via a display associated with theedge-computing device.

According to some embodiments, the non-transitory computer-readablemedium, that upon execution of the processor, further causes theprocessor to perform: determining a comparison result by at leastcomparing, using the edge-computing device, the hybrid epoch score ofthe user to hybrid epoch scores of one or more related users; andpresenting the comparison result to the user via a display associatedwith the edge-computing device.

According to some embodiments, the non-transitory computer-readablemedium, that upon execution of the processor, further causes theprocessor to perform: detecting, using one or more sensors associatedwith the edge-computing device, whether the vehicle is being operated bythe user; and collecting, in response to detecting that the vehicle isbeing operated by the user, telematics data using the one or moresensors.

Additional Considerations According to Certain Embodiments

The following additional considerations apply to the foregoingdiscussion. Throughout this specification, plural instances mayimplement components, operations, or structures described as a singleinstance. Although individual operations of one or more methods areillustrated and described as separate operations, one or more of theindividual operations may be performed concurrently, and nothing needsthat the operations be performed in the order illustrated. Structuresand functionality presented as separate components in exampleconfigurations may be implemented as a combined structure or component.Similarly, structures and functionality presented as a single componentmay be implemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Additionally, certain embodiments are described herein as includinglogic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (e.g., code embodiedon a machine-readable medium or in a transmission signal) or hardware.In hardware, the routines, etc., are tangible units capable ofperforming certain operations and may be configured or arranged in acertain manner. In example embodiments, one or more computer systems(e.g., a standalone, client or server computer system) or one or morehardware modules of a computer system (e.g., a processor or a group ofprocessors) may be configured by software (e.g., an application orapplication portion) as a hardware module that operates to performcertain operations as described herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. In some examples, a hardware module maycomprise dedicated circuitry or logic that is permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC)) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. Considering embodiments inwhich hardware modules are temporarily configured (e.g., programmed),each of the hardware modules need not be configured or instantiated atany one instance in time. In some examples, where the hardware modulescomprise a general-purpose processor configured using software, thegeneral-purpose processor may be configured as respective differenthardware modules at different times. Software may accordingly configurea processor, in some examples, to constitute a particular hardwaremodule at one instance of time and to constitute a different hardwaremodule at a different instance of time.

Hardware modules may provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, insome examples, through the storage and retrieval of information inmemory structures to which the multiple hardware modules have access. Insome examples, one hardware module may perform an operation and storethe output of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and may operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. In some examples, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location, while in certain embodiments theprocessors may be distributed across a number of locations.

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In certainembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Those of ordinary skill in the art will recognize that a wide variety ofmodifications, alterations, and combinations can be made with respect tothe above described embodiments without departing from the scope of thedisclosure, and that such modifications, alterations, and combinationsare to be viewed as being within the ambit of the inventive concept.

Additional Considerations According to Various Embodiments

In certain embodiments, some or all components of various embodiments ofthe present disclosure each are, individually and/or in combination withat least another component, implemented using one or more softwarecomponents, one or more hardware components, and/or one or morecombinations of software and hardware components. As an example, some orall components of various embodiments of the present disclosure eachare, individually and/or in combination with at least another component,implemented in one or more circuits, such as one or more analog circuitsand/or one or more digital circuits. In some examples, while theembodiments described above refer to particular features, the scope ofthe present disclosure also includes embodiments having differentcombinations of features and embodiments that do not include all of thedescribed features. As an example, various embodiments and/or examplesof the present disclosure can be combined.

Additionally, the methods and systems described herein may beimplemented on many different types of processing devices by programcode comprising program instructions that are executable by the deviceprocessing subsystem. The software program instructions may includesource code, object code, machine code, or any other stored data that isoperable to cause a processing system to perform the methods andoperations described herein. Certain implementations may also be used,however, such as firmware or even appropriately designed hardwareconfigured to perform the methods and systems described herein.

The systems' and methods' data (e.g., associations, mappings, datainput, data output, intermediate data results, final data results) maybe stored and implemented in one or more different types ofcomputer-implemented data stores, such as different types of storagedevices and programming constructs (e.g., RAM, ROM, EEPROM, Flashmemory, flat files, databases, programming data structures, programmingvariables, IF-THEN (or similar type) statement constructs, applicationprogramming interface). It is noted that data structures describeformats for use in organizing and storing data in databases, programs,memory, or other computer-readable media for use by a computer program.

The systems and methods may be provided on many different types ofcomputer-readable media including computer storage mechanisms (e.g.,CD-ROM, diskette, RAM, flash memory, computer's hard drive, DVD) thatcontain instructions (e.g., software) for use in execution by aprocessor to perform the methods' operations and implement the systemsdescribed herein. The computer components, software modules, functions,data stores and data structures described herein may be connecteddirectly or indirectly to each other in order to allow the flow of dataneeded for their operations. It is also noted that a module or processorincludes a unit of code that performs a software operation, and can beimplemented for example as a subroutine unit of code, or as a softwarefunction unit of code, or as an object (as in an object-orientedparadigm), or as an applet, or in a computer script language, or asanother type of computer code. The software components and/orfunctionality may be located on a single computer or distributed acrossmultiple computers depending upon the situation at hand.

The computing system can include client devices and servers. A clientdevice and server are generally remote from each other and typicallyinteract through a communication network. The relationship of clientdevice and server arises by virtue of computer programs running on therespective computers and having a client device-server relationship toeach other.

This specification contains many specifics for particular embodiments.Certain features that are described in this specification in the contextof separate embodiments can also be implemented in combination in asingle embodiment. Conversely, various features that are described inthe context of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations, one or more features from a combination can in some casesbe removed from the combination, and a combination may, in someexamples, be directed to a subcombination or variation of asubcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

Although specific embodiments of the present disclosure have beendescribed, it will be understood by those of skill in the art that thereare certain embodiments that are equivalent to the describedembodiments. Accordingly, it is to be understood that the presentdisclosure is not to be limited by the specific illustrated embodiments.

1. A computer-implemented method for generating a hybrid epoch score for a user using edge-computing, the method comprising: retrieving, by an edge-computing device, prior telematics data indicative of the operation of a vehicle by the user during one or more prior trips in a prior epoch; collecting, using one or more sensors of the edge-computing device, recent telematics data indicative of the operation of the vehicle by the user during one or more recent trips in a current epoch; generating, using the edge-computing device, a prior epoch score based at least in part upon the prior telematics data; generating, using the edge-computing device, a partial current epoch score based at least in part upon the recent telematics data; generating, using the edge-computing device, a hybrid current epoch score based at least in part upon the prior epoch score and the partial current epoch score; determining, using the edge-computing device, a comparison result by at least comparing the hybrid current epoch score of the user to hybrid current epoch scores of one or more related users; and presenting the comparison result to the user via a display associated with the edge-computing device.
 2. The computer-implemented method of claim 1, wherein: the prior epoch is associated with a prior epoch time; the one or more recent trips is associated with a recent driving time and an elapsed time, the recent driving time being the amount of time the vehicle was under operation in the current epoch, the elapsed time being the amount of time that has passed in the current epoch; and the generating the hybrid current epoch score includes: determining, using the edge-computing device, a remaining time of the current epoch as the prior epoch time minus the elapsed time; and determining, using the edge-computing device, the hybrid current epoch score based at least in part upon adding: the prior epoch score multiplied by the remaining time; and the partial current epoch score multiplied by the recent driving time.
 3. The computer-implemented method of claim 1, wherein: the current epoch is associated with a plurality of time segments; each recent trip of the plurality of recent trips is associated with one or more time segments of the plurality of time segments; the generating the partial current epoch score includes: assigning, using the edge-computing device, each segment of the plurality of time segments with the prior epoch score; generating, for each recent trip of the plurality of recent trips using the edge-computing device, a partial current epoch score based at least in part upon the recent telematics data; and updating, for each recent trip of the plurality of recent trips using the edge-computing device, the associated one or more time segments with the associated partial current epoch score; and the generating the hybrid current epoch score includes generating, using the edge-computing device, the hybrid current epoch score based at least in part upon: the partial current epoch score for each segment of the plurality of time segments that was updated; and the prior epoch score for each segment of the plurality of time segments that was not updated.
 4. The computer-implemented method of claim 1, further comprising retrieving, from at least one of the edge-computing device and the server, historic telematics data indicative of the operation of the vehicle by the user during one or more historic trips in one or more historic epochs; and determining, using the edge-computing device, a historic epoch time based at least in part upon the historic telematics data, the historic epoch time being the average total time of a historic epoch of the one or more historic epochs; wherein: the one or more recent trips is associated with a recent driving time and an elapsed time, the recent driving time being the amount of time the vehicle was under operation in the current epoch, the elapsed time being the amount of time that has passed in the current epoch; the generating the hybrid current epoch score includes: determining, using the edge-computing device, a remaining time of the current epoch as the historic epoch time minus the elapsed time; and determining, using the edge-computing device, the hybrid current epoch score based at least in part upon adding: the partial current epoch score multiplied by a ratio of the elapsed time to a time historically driven per epoch; and the prior epoch score multiplied by a ratio of the remaining time to the time historically driven per epoch.
 5. The computer-implemented method of claim 1, wherein: the prior telematics data include: prior qualitative data indicative of one or more prior driving behaviors of the user during the one or more prior trips; and prior quantitative data indicative of a prior driving time in the prior epoch; the recent telematics data include: recent qualitative data indicative of one or more recent driving behaviors of the user during the one or more recent trips; and recent quantitative data indicative of a recent driving time in the current epoch; the generating the prior epoch score includes generating, using the edge-computing device, the prior epoch score based at least in part upon the prior qualitative data and the prior quantitative data; the generating the partial current epoch score includes generating, using the edge-computing device, the partial current epoch score based at least in part upon the recent qualitative data and the recent quantitative data; and the generating the hybrid current epoch score includes generating, using the edge-computing device, the hybrid current epoch score based at least in part upon the prior qualitative data, the prior quantitative data, the recent qualitative data, and the recent quantitative data.
 6. The computer-implemented method of claim 1, wherein the: collecting the recent telematics data includes collecting, using one or more sensors of the edge-computing device, the recent telematics data at least one of in real-time, in near real-time, and/or continuously; generating the partial current epoch score includes generating, using the edge-computing device, the partial current epoch score at least one of in real-time, in near real-time, and/or continuously; and generating the hybrid current epoch score includes generating, using the edge-computing device, the hybrid current epoch score at least one of in real-time, in near real-time, and/or continuously.
 7. The computer-implemented method of claim 1, further comprising: retrieving third-party information associated with at least one of the one or more prior trips and the one or more recent trips; wherein at least one of the: generating the prior epoch score includes generating, using the edge-computing device, the prior epoch score based at least in part upon the prior telematics data and the third-party information; and generating the partial current epoch score includes generating, using the edge-computing device, the partial current epoch score based at least in part upon the recent telematics data and the third-party information.
 8. The computer-implemented method of claim 1, further comprising: presenting the hybrid current epoch score to the user via a display associated with the edge-computing device.
 9. The computer-implemented method of claim 1, further comprising: determining, using the edge-computing device, a rate or discount for an associated product or an associated service based at least in part upon the hybrid current epoch score; and presenting the rate or discount to the user via a display associated with the edge-computing device.
 10. (canceled)
 11. The computer-implemented method of claim 1, further comprising: detecting, using the one or more sensors associated with the edge-computing device, whether the vehicle is being operated by the user; and collecting, in response to detecting that the vehicle is being operated by the user, telematics data using the one or more sensors.
 12. The computer-implemented method of claim 1, wherein at least one of the: generating the prior epoch score includes generating, using a neural network of the edge-computing device, the prior epoch score based at least in part upon the prior telematics data; generating the partial current epoch score includes generating, using a neural network of the edge-computing device, the partial current epoch score based at least in part upon the recent telematics data; and generating the hybrid current epoch score includes generating, using a neural network of the edge-computing device, the hybrid current epoch score based at least in part upon the prior epoch score and the partial current epoch score.
 13. A system for generating a hybrid epoch score for a user, the system comprising: a data retrieving module configured to: retrieve, by an edge-computing device and a server, prior telematics data indicative of the operation of a vehicle by the user during one or more prior trips in a prior epoch; a data collecting module configured to: collect, using one or more sensors of the edge-computing device, recent telematics data indicative of the operation of the vehicle by the user during one or more recent trips in a current epoch; and an epoch score generating module configured to: generate, using the edge-computing device, a prior epoch score based at least in part upon the prior telematics data; generate, using the edge-computing device, a partial current epoch score based at least in part upon the recent telematics data; generate, using the edge-computing device, a hybrid current epoch score based at least in part upon the prior epoch score and the partial current epoch score; determining, using the edge-computing device, a comparison result by at least comparing the hybrid current epoch score of the user to hybrid current epoch scores of one or more related users; and presenting the comparison result to the user via a display associated with the edge-computing device.
 14. The system of claim 13, wherein: the prior epoch is associated with a prior epoch time; the one or more recent trips is associated with a recent driving time and an elapsed time, the recent driving time being the amount of time the vehicle was under operation in the current epoch, the elapsed time being the amount of time that has passed in the current epoch; and the epoch score generating module is configured to generate the hybrid current epoch score by at least: determining, using the edge-computing device, a remaining time of the current epoch as the prior epoch time minus the elapsed time; and determining, using the edge-computing device, the hybrid current epoch score based at least in part upon adding: the prior epoch score multiplied by the remaining time; and the partial current epoch score multiplied by the recent driving time.
 15. The system of claim 13, wherein: the current epoch is associated with a plurality of time segments; each recent trip of the plurality of recent trips is associated with one or more time segments of the plurality of time segments; the epoch score generating module is configured to: generate the partial current epoch score by at least: assigning, using the edge-computing device, each segment of the plurality of time segments with the prior epoch score; generating, for each recent trip of the plurality of recent trips using the edge-computing device, a partial current epoch score based at least in part upon the recent telematics data; and updating, for each recent trip of the plurality of recent trips using the edge-computing device, the associated one or more time segments with the associated partial current epoch score; and generate, using the edge-computing device, the hybrid current epoch score based at least in part upon: the partial current epoch score for each segment of the plurality of time segments that was updated; and the prior epoch score for each segment of the plurality of time segments that was not updated.
 16. The system of claim 13, wherein: the data retrieving module is further configured to retrieve, from at least one of the edge-computing device and the server, historic telematics data indicative of the operation of the vehicle by the user during one or more historic trips in one or more historic epochs; the one or more recent trips is associated with a recent driving time and an elapsed time, the recent driving time being the amount of time the vehicle was under operation in the current epoch, the elapsed time being the amount of time that has passed in the current epoch; and the epoch score generating module is configured to: determine, using the edge-computing device, a historic epoch time based at least in part upon the historic telematics data, the historic epoch time being the average total time of a historic epoch of the one or more historic epochs; generate, using the edge-computing device, the hybrid current epoch score by at least: determining, using the edge-computing device, a remaining time of the current epoch as the historic epoch time minus the elapsed time; and determining, using the edge-computing device, the hybrid current epoch score based at least in part upon adding: the partial current epoch score multiplied by a ratio of the elapsed time to a time historically driven per epoch; and the prior epoch score multiplied by a ratio of the remaining time to the time historically driven per epoch.
 17. A non-transitory computer-readable medium with instructions stored thereon, that upon execution by a processor, causes the processor to perform: retrieving, by an edge-computing device, prior telematics data indicative of the operation of a vehicle by the user during one or more prior trips in a prior epoch; collecting, using one or more sensors of the edge-computing device, recent telematics data indicative of the operation of the vehicle by the user during one or more recent trips in a current epoch; generating, using the edge-computing device, a prior epoch score based at least in part upon the prior telematics data; generating, using the edge-computing device, a partial current epoch score based at least in part upon the recent telematics data; generating, using the edge-computing device, a hybrid current epoch score based at least in part upon the prior epoch score and the partial current epoch score; determining, using the edge-computing device, a comparison result by at least comparing the hybrid current epoch score of the user to hybrid current epoch scores of one or more related users; and presenting the comparison result to the user via a display associated with the edge-computing device.
 18. The non-transitory computer-readable medium of claim 17, wherein: the prior epoch is associated with a prior epoch time; the one or more recent trips is associated with a recent driving time and an elapsed time, the recent driving time being the amount of time the vehicle was under operation in the current epoch, the elapsed time being the amount of time that has passed in the current epoch; and the generating the hybrid current epoch score includes: determining, using the edge-computing device, a remaining time of the current epoch as the prior epoch time minus the elapsed time; and determining, using the edge-computing device, the hybrid current epoch score based at least in part upon adding: the prior epoch score multiplied by the remaining time; and the partial current epoch score multiplied by the recent driving time.
 19. The non-transitory computer-readable medium of claim 17, wherein: the current epoch is associated with a plurality of time segments; each recent trip of the plurality of recent trips is associated with one or more time segments of the plurality of time segments; the generating the partial current epoch score includes: assigning, using the edge-computing device, each segment of the plurality of time segments with the prior epoch score; generating, for each recent trip of the plurality of recent trips using the edge-computing device, a partial current epoch score based at least in part upon the recent telematics data; and updating, for each recent trip of the plurality of recent trips using the edge-computing device, the associated one or more time segments with the associated partial current epoch score; and the generating the hybrid current epoch score includes generating, using the edge-computing device, the hybrid current epoch score based at least in part upon: the partial current epoch score for each segment of the plurality of time segments that was updated; and the prior epoch score for each segment of the plurality of time segments that was not updated.
 20. The non-transitory computer-readable medium of claim 17, that upon execution of the processor, further causes the processor to perform: retrieving, from at least one of the edge-computing device and the server, historic telematics data indicative of the operation of the vehicle by the user during one or more historic trips in one or more historic epochs; and determining, using the edge-computing device, a historic epoch time based at least in part upon the historic telematics data, the historic epoch time being the average total time of a historic epoch of the one or more historic epochs; wherein: the one or more recent trips is associated with a recent driving time and an elapsed time, the recent driving time being the amount of time the vehicle was under operation in the current epoch, the elapsed time being the amount of time that has passed in the current epoch; the generating the hybrid current epoch score includes: determining, using the edge-computing device, a remaining time of the current epoch as the historic epoch time minus the elapsed time; and determining, using the edge-computing device, the hybrid current epoch score based at least in part upon adding: the partial current epoch score multiplied by a ratio of the elapsed time to a time historically driven per epoch; and the prior epoch score multiplied by a ratio of the remaining time to the time historically driven per epoch. 